custom-written perl algorithm  (ActiveState Software Inc)

Journal: Scientific Reports

Article Title: Comparative Transcriptomic Analysis of Multiple Cardiovascular Fates from Embryonic Stem Cells Predicts Novel Regulators in Human Cardiogenesis

doi: 10.1038/srep09758

Figure Lengend Snippet: GEPA algorithm identified lineage-enriched genes from RNA-seq data. ( a ) Schematic representation of the “GEPA” algorithm workflow to identify the lineage-enriched patterns of the genes. ( b ) Estimation of false positive and false negative ratio of GEPA algorithm at different thresholds of FPKM fold change. ( c ) Distribution of the genes across the expression pattern categories. Lineage-enriched patterns are indicated at left side. In the same row, rectangles filled with blue are at least 2.5 fold higher than those in light gray. The bars indicating the number of genes are color coded. Blue for single lineage-enriched groups. Green, yellow and orange for two, three and four lineage-enriched groups, respectively. Light blue for “Gradient” group and purple for “Even” group. ( d ) qRT-PCR validation of the signature genes for lineage-enriched categories. Gene name and expression pattern defined by GEPA (in brackets) were shown above the plots.

Article Snippet: Lists of predicted cardiac development regulators based on histone modification markers and expression levels were from Paige et al . . A Perl module implementing the GEPA algorithm can be accessed through http://sourceforge.net/projects/gepa/files/ .

Techniques: RNA Sequencing, Expressing, Quantitative RT-PCR, Biomarker Discovery

Journal: Scientific Reports

Article Title: Comparative Transcriptomic Analysis of Multiple Cardiovascular Fates from Embryonic Stem Cells Predicts Novel Regulators in Human Cardiogenesis

doi: 10.1038/srep09758

Figure Lengend Snippet: LEGs identified by GEPA predict novel regulatory genes and pathways in human cardiovascular differentiation. ( a ) Percent of the genes with or without annotation in “cardiovascular development and function” from Ingenuity knowledge database in the LEG groups identified by GEPA. ( b ) Our GEPA analysis of top 100 novel cardiac regulatory genes previously predicted by Paige et al. when considering “expression only” or “H3K4me3+H3K27me3+expression “ at T5, T9 and T14 of CM differentiation, respectively. ( c ) Examples of predicted functional genes by GEPA. Dynamic expression of these genes is shown in heatmap. Known cardiac regulators are highlighted in blue. Our predicted candidates, which are overlapping with chromatin dynamics-based predictions by Paige et al. and Wamstad et al ., are shown in green . Novel regulatory genes solely predicted by GEPA are labeled in red. ( d ) Predicted novel regulatory pathways in cardiovascular differentiation using GEPA and Inginuity IPA pathway enrichment analysis. ( e ) A heat-map showing the lineage-specific expression pattern of ephrin and ephrin receptor genes during cardiovascular differentiation. To indicate the lineage-specificity, the relative gene expression was shown as percent in the sum of all the cell types in the heatmap. ( f ) Illustrative Ephrin/Ephrin signaling pathway imposed on a pathway map based on Ingenuity IPA showing localizations of the LEGs. Color code of the molecule indicates its lineage specificity. Blue indicates enrichment in “MCP” ; Orange, both “MCP” and “MCP&CM”; Green, “MCP&CM”.

Article Snippet: Lists of predicted cardiac development regulators based on histone modification markers and expression levels were from Paige et al . . A Perl module implementing the GEPA algorithm can be accessed through http://sourceforge.net/projects/gepa/files/ .

Techniques: Expressing, Functional Assay, Labeling, Gene Expression

Journal: Nucleic Acids Research

Article Title: Visualizing helicases unwinding DNA at the single molecule level

doi: 10.1093/nar/gkq173

Figure Lengend Snippet: DNA unwinding by AddA N B N is characterized by pauses and bursts of activity. ( A ) Example of how a single time-course can be segmented into pauses and unwinding phases. The raw intensity data (light blue open circle) were smoothed by running average using an optimum window size (shown as pink and dark blue lines). Then, the first derivative at every time point was calculated (grey open circle). If the derivative trace (grey open circle) is smoothed using an median filter (black line), then the histogram of the smoothed derivative values (inset) reveals two populations, one corresponding the pauses (clustered around zero) and one to the unwinding phases (clustered around 17 cpp). Thresholding ( Thr. ) of the derivative data (dashed line) allows the original intensity trace to be segmented into pauses (< Thr. , pink lines) and unwinding phases (> Thr. , dark lines). ( B–E ) To automatically analyze all data, a custom-written PERL algorithm was used (see ‘Materials and methods’ section). Using this algorithm, the distributions of the number of pauses per event (B), the duration of pauses (C), maximum rate of unwinding (D) and duration of unwinding phase (E) were obtained for 1 mM ATP (circle) and 3 μM ATP (triangle). All distributions are presented as percentage frequency, normalized to the maximum.

Article Snippet: To identify and characterize the pauses and unwinding phases a custom-written PERL algorithm (ActiveState Software Inc.) was developed.

Techniques: Activity Assay