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perl algorithm  (ActiveState Software Inc)

 
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    ActiveState Software Inc perl algorithm
    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 <t>PERL</t> 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.
    Perl Algorithm, supplied by ActiveState Software Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/perl+algorithm/perl+program/pmc02910053-69-10-13
    Average 90 stars, based on 1 article reviews
    perl algorithm - by Bioz Stars, 2026-09
    90/100 stars

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    1) Product Images from "Visualizing helicases unwinding DNA at the single molecule level"

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

    Journal: Nucleic Acids Research

    doi: 10.1093/nar/gkq173

    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.
    Figure Legend 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.

    Techniques Used: Activity Assay

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    Article Title: ThermoPhyl: a software tool for selecting phylogenetically optimized conventional and quantitative-PCR taxon-targeted assays for use with complex samples.
    Article Snippet: ThermoPhyl, a freeware PERL script program (download PERL from http://www.activestate.com/activeperl), is a simple matching program that runs on both windows and Unix machines.

    Article Title: Visualizing helicases unwinding DNA at the single molecule level
    Article Snippet: To identify and characterize the pauses and unwinding phases a custom-written PERL algorithm (ActiveState Software Inc.) was developed.

    Gene Expression:

    Article Title: A method for visualization of “omic” datasets for sphingolipid metabolism to predict potentially interesting differences
    Article Snippet: .. Supplement 1A: Perl program to extract normalized glycosphingolipid gene expression values #!/usr/bin/Perl # microarray filtering file use strict; use warnings; my $Micro = $ARGV[0]; # Microarray file neme my $GeneList = $ARGV[1]; # Gene or probelist file open(MICRO, $Micro)||die"cannot open microarray file\n"; open(LIST, $GeneList)||die"cannot open genelist file\n"; my %hash= (); while(my $line = ){ chomp($line); my @words = split(/\t/,$line); my $id = $words[0]; $hash{$id} = 0; } my @keys = keys(%hash); my $num = @keys; while(my $gene = ) { chomp($gene); for(my $i=0; $i < $num; $i++) { my $key = $keys[$i]; chomp($key); if ($gene =~ m/$key/i) { print "$gene \n"; } } } close(MICRO); close(LIST); Instructions for using Perl program for filtering sphingolipid genes: on windows PC install the Activeperl package from www.activestate.com (Perl is already installed on Mac OS in the X‐terminal). ..

    Microarray:

    Article Title: A method for visualization of “omic” datasets for sphingolipid metabolism to predict potentially interesting differences
    Article Snippet: .. Supplement 1A: Perl program to extract normalized glycosphingolipid gene expression values #!/usr/bin/Perl # microarray filtering file use strict; use warnings; my $Micro = $ARGV[0]; # Microarray file neme my $GeneList = $ARGV[1]; # Gene or probelist file open(MICRO, $Micro)||die"cannot open microarray file\n"; open(LIST, $GeneList)||die"cannot open genelist file\n"; my %hash= (); while(my $line = ){ chomp($line); my @words = split(/\t/,$line); my $id = $words[0]; $hash{$id} = 0; } my @keys = keys(%hash); my $num = @keys; while(my $gene = ) { chomp($gene); for(my $i=0; $i < $num; $i++) { my $key = $keys[$i]; chomp($key); if ($gene =~ m/$key/i) { print "$gene \n"; } } } close(MICRO); close(LIST); Instructions for using Perl program for filtering sphingolipid genes: on windows PC install the Activeperl package from www.activestate.com (Perl is already installed on Mac OS in the X‐terminal). ..



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    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 <t>PERL</t> 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.
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    Image Search Results


    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.

    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

    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”.

    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

    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.

    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