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softberry-bprom algorithm  (Softberry Inc)

 
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    Structured Review

    Softberry Inc softberry-bprom algorithm
    Softberry Bprom Algorithm, supplied by Softberry 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/softberry+bprom+algorithm/bprom+algorithm/pmc08034623-128-9-12
    Average 90 stars, based on 1 article reviews
    softberry-bprom algorithm - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    In Silico:

    Article Title: Characterization of salA , syrF , and syrG Genes and Attendant Regulatory Networks Involved in Plant Pathogenesis by Pseudomonas syringae pv. syringae B728a
    Article Snippet: Nucleotide sequences that were 100-bp upstream of identified transcriptional start sites were analyzed using the Softberry Bprom algorithm ( http://linux1.softberry.com/berry.phtml ) to identify putative σ 70 -dependent promoters.

    Expressing:

    Article Title: Characterization of salA , syrF , and syrG Genes and Attendant Regulatory Networks Involved in Plant Pathogenesis by Pseudomonas syringae pv. syringae B728a
    Article Snippet: Nucleotide sequences that were 100-bp upstream of identified transcriptional start sites were analyzed using the Softberry Bprom algorithm ( http://linux1.softberry.com/berry.phtml ) to identify putative σ 70 -dependent promoters.

    Western Blot:

    Article Title: Characterization of salA , syrF , and syrG Genes and Attendant Regulatory Networks Involved in Plant Pathogenesis by Pseudomonas syringae pv. syringae B728a
    Article Snippet: Nucleotide sequences that were 100-bp upstream of identified transcriptional start sites were analyzed using the Softberry Bprom algorithm ( http://linux1.softberry.com/berry.phtml ) to identify putative σ 70 -dependent promoters.

    Staining:

    Article Title: Characterization of salA , syrF , and syrG Genes and Attendant Regulatory Networks Involved in Plant Pathogenesis by Pseudomonas syringae pv. syringae B728a
    Article Snippet: Nucleotide sequences that were 100-bp upstream of identified transcriptional start sites were analyzed using the Softberry Bprom algorithm ( http://linux1.softberry.com/berry.phtml ) to identify putative σ 70 -dependent promoters.

    Incubation:

    Article Title: Characterization of salA , syrF , and syrG Genes and Attendant Regulatory Networks Involved in Plant Pathogenesis by Pseudomonas syringae pv. syringae B728a
    Article Snippet: Nucleotide sequences that were 100-bp upstream of identified transcriptional start sites were analyzed using the Softberry Bprom algorithm ( http://linux1.softberry.com/berry.phtml ) to identify putative σ 70 -dependent promoters.

    Software:

    Article Title: Characterization of salA , syrF , and syrG Genes and Attendant Regulatory Networks Involved in Plant Pathogenesis by Pseudomonas syringae pv. syringae B728a
    Article Snippet: Nucleotide sequences that were 100-bp upstream of identified transcriptional start sites were analyzed using the Softberry Bprom algorithm ( http://linux1.softberry.com/berry.phtml ) to identify putative σ 70 -dependent promoters.

    Sequencing:

    Article Title: Characterization of salA , syrF , and syrG Genes and Attendant Regulatory Networks Involved in Plant Pathogenesis by Pseudomonas syringae pv. syringae B728a
    Article Snippet: Nucleotide sequences that were 100-bp upstream of identified transcriptional start sites were analyzed using the Softberry Bprom algorithm ( http://linux1.softberry.com/berry.phtml ) to identify putative σ 70 -dependent promoters.



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    softberry bprom algorithm  (Softberry Inc)
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    Identification of tpxD regulatory promoter elements. (A) In silico analysis of tpxD proximal promoter region. Identification of tpxD promoter elements using <t>BPROM</t> <t>algorithm:</t> ATG start codon (1); Shine Dalgarno (2); transcription start site at A (3); −10 and −35 elements of the σ 70 promoter (4 and 5, respectively). The first nucleotide included in each of the Δ tpxD comp mutants (1–6) is indicated by arrow. (B) Influence of tpxD putative regulatory elements on TpxD expression levels under H 2 O 2 stress. TpxD levels, determined by Western blotting, were measured in D39, Δ tpxD and Δ tpxD comp1-6 (described in Figure ). Bacteria were grown anaerobically to OD 620 = 0.25 and then challenged with 1 mM H 2 O 2 for 40 min. Separated proteins were electroblotted onto 0.45 μm nitrocellulose membrane, and stained with Ponceau S to ensure equal loading, before incubation with antibodies. Membranes were exposed for long time intervals to ensure the absence of bands in Δ tpxD comp1-4 and are lined up, as indicated in the figure. Densitometry was performed using ImageJ software.
    Softberry Bprom Algorithm, supplied by Softberry 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/softberry+bprom+algorithm/bprom+algorithm/pmc05443158-112-7-6
    Average 90 stars, based on 1 article reviews
    softberry bprom algorithm - by Bioz Stars, 2026-09
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    Image Search Results


    Identification of tpxD regulatory promoter elements. (A) In silico analysis of tpxD proximal promoter region. Identification of tpxD promoter elements using BPROM algorithm: ATG start codon (1); Shine Dalgarno (2); transcription start site at A (3); −10 and −35 elements of the σ 70 promoter (4 and 5, respectively). The first nucleotide included in each of the Δ tpxD comp mutants (1–6) is indicated by arrow. (B) Influence of tpxD putative regulatory elements on TpxD expression levels under H 2 O 2 stress. TpxD levels, determined by Western blotting, were measured in D39, Δ tpxD and Δ tpxD comp1-6 (described in Figure ). Bacteria were grown anaerobically to OD 620 = 0.25 and then challenged with 1 mM H 2 O 2 for 40 min. Separated proteins were electroblotted onto 0.45 μm nitrocellulose membrane, and stained with Ponceau S to ensure equal loading, before incubation with antibodies. Membranes were exposed for long time intervals to ensure the absence of bands in Δ tpxD comp1-4 and are lined up, as indicated in the figure. Densitometry was performed using ImageJ software.

    Journal: Frontiers in Cellular and Infection Microbiology

    Article Title: CodY Regulates Thiol Peroxidase Expression as Part of the Pneumococcal Defense Mechanism against H 2 O 2 Stress

    doi: 10.3389/fcimb.2017.00210

    Figure Lengend Snippet: Identification of tpxD regulatory promoter elements. (A) In silico analysis of tpxD proximal promoter region. Identification of tpxD promoter elements using BPROM algorithm: ATG start codon (1); Shine Dalgarno (2); transcription start site at A (3); −10 and −35 elements of the σ 70 promoter (4 and 5, respectively). The first nucleotide included in each of the Δ tpxD comp mutants (1–6) is indicated by arrow. (B) Influence of tpxD putative regulatory elements on TpxD expression levels under H 2 O 2 stress. TpxD levels, determined by Western blotting, were measured in D39, Δ tpxD and Δ tpxD comp1-6 (described in Figure ). Bacteria were grown anaerobically to OD 620 = 0.25 and then challenged with 1 mM H 2 O 2 for 40 min. Separated proteins were electroblotted onto 0.45 μm nitrocellulose membrane, and stained with Ponceau S to ensure equal loading, before incubation with antibodies. Membranes were exposed for long time intervals to ensure the absence of bands in Δ tpxD comp1-4 and are lined up, as indicated in the figure. Densitometry was performed using ImageJ software.

    Article Snippet: Promoter elements were predicted using the Softberry BPROM algorithm of bacterial promoters ( http://www.softberry.com/berry.phtml?topic=bprom&group=programs&subgroup=gfindb ).

    Techniques: In Silico, Expressing, Western Blot, Staining, Incubation, Software