Transcription factor binding sites detection by using alignment-based approach

Mahdevar, Ghasem; Sadeghi, Mehdi; Nowzari-Dalini, Abbas

doi:10.1016/j.jtbi.2012.03.039

Cited by 4 publications

(8 citation statements)

References 25 publications

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“…Where, TFBSs range from 6 to 20 base pairs in length and have 5 to 10 instances with 60% to 90% identity. Furthermore, AmotiF gained the highest score in the famous assessment provided by Tompa et al (2005), as stated by Mahdevar et al (2012). By investigating TFBSs of S. cerevisiae Promoter Database (SCPD) (Zhu and Zhang, 1999) and TFBSs of Escherichia coli Database (RegulonDB) (Salgado et al, 2004), we have found that TFBSs positions are far from being random, to be exact, TFBSs positions have little standard deviation: 28% of maximum value, which is consistent with the results of Beer and Tavazoie (2004).…”

Section: Methodssupporting

confidence: 80%

“…Certainly, many computationally TFBSs discovery methods are available; for an overview of these methods see (MacIsaac and Fraenkel, 2006;Tompa et al, 2005). Among them, AmotiF tool (Mahdevar et al, 2012) has good sensitivity and specificity, runs fast, and is planned to find overrepresented pattern with respect to the biological facts about TFBSs (See Mahdevar et al (2012) for more details about this method). Therefore, we have chosen AmotiF tool to find TFBSs.…”

Section: Methodsmentioning

confidence: 99%

“…Experiments with other TFBSs discovery methods confirm the validity of this choice: AmotiF achieved the best accuracy in the discovery of random TFBSs planted in a set of sequences. Precisely, the accuracy of AlignACE (Roth et al, 1998), AmotiF (Mahdevar et al, 2012), BioProspector (Liu et al, 2001), and MEME (Bailey and Elkan, 1995) in finding 10 TFBSs planted in 100 random sequences of 500 nucleotides was 39%, 62%, 56%, and 53%. Where, TFBSs range from 6 to 20 base pairs in length and have 5 to 10 instances with 60% to 90% identity.…”

Section: Methodsmentioning

confidence: 99%

“…This step is mainly dependent upon the successful binding of Transcription Factors (TFs) proteins to explicit positions in genes upstream or promoters, also known as TF Binding Sites (TFBSs) (Chua et al, 2004;B. Lewin, 2004;Mahdevar et al, 2012). In general, 4% of all genes produce TFs and usually multiple TFs act together to regulate the expression of a given gene (Bar-Joseph et al, 2003;Schlitt and Brazma, 2007;Yu et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…In general, 4% of all genes produce TFs and usually multiple TFs act together to regulate the expression of a given gene (Bar-Joseph et al, 2003;Schlitt and Brazma, 2007;Yu et al, 2003). Unfortunately, finding these TFs and their corresponding TFBSs is costly and laborious; therefore, computational discovery of TFs and TFBSs has attracted considerable attention (Bailey and Elkan, 1994;GuhaThakurta, 2006;Hertz and Stormo, 1999;Mahdevar et al, 2012;Thijs et al, 2002;Tompa et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

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Inferring gene correlation networks from transcription factor binding sites

2013

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Gene expression is a highly regulated biological process that is fundamental to the existence of phenotypes of any living organism. The regulatory relations are usually modeled as a network; simply, every gene is modeled as a node and relations are shown as edges between two related genes. This paper presents a novel method for inferring correlation networks, networks constructed by connecting coexpressed genes, through predicting co-expression level from genes promoter's sequences. According to the results, this method works well on biological data and its outcome is comparable to the methods that use microarray as input. The method is written in C++ language and is available upon request from the corresponding author.

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Section: Methodssupporting

confidence: 80%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Inferring gene correlation networks from transcription factor binding sites

2013

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RETRACTED ARTICLE: LncRNA LINC00520 aggravates cell proliferation and migration in lung adenocarcinoma via a positive feedback loop

Huang

Wang

et al. 2021

BMC Pulm Med

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Background Lung adenocarcinoma (LUAD) is the most common histological subtype of primary lung cancer. To identify the biomarker of diagnosis for LUAD is of great significance. Long non-coding RNAs (lncRNAs) were previously revealed to exert vital effects in numerous cancers. LncRNA long intergenic non-protein coding RNA 520 (LINC00520) served as an oncogene in various cancers. Therefore, our study was specially designed to probe the role of LINC00520 in LUAD. Results LINC00520 expression was detected by RT-qPCR. Next, function of LINC00520 in LUAD was verified by in vitro loss-of-function experiments. DNA pull down, ChIP, RIP, and luciferase reporter assays were conducted to reveal the regulatory mechanism of LINC00520. We found that LINC00520 was upregulated in LUAD. Additionally, LINC00520 upregulation is associated with the poor prognosis for patients with LUAD. Furthermore, LINC00520 downregulation suppressed LUAD cell proliferation and migration and induced cell apoptosis. Forkhead box P3 (FOXP3) is identified as the transcription factor to transcriptionally activate LINC00520. Moreover, LINC00520 positively upregulated FOXP3 expression via sponging miR-3611 in LUAD cells. Subsequently, rescue experiments delineated that miR-3611 downregulation or FOXP3 overexpression reversed the effects of silenced LINC00520 on proliferative and migratory capabilities in LUAD cells. Conclusion This study innovatively indicated that lncRNA LINC00520 facilitated cell proliferative and migratory abilities in LUAD through interacting with miR-3611 and targeting FOXP3, which may provide a potential novel insight for treatment of LUAD.

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LncRNA LINC00520 Aggravates Cell Proliferation and Migration in Lung Adenocarcinoma via a Positive Feedback Loop

Huang

Wang

et al. 2021

Preprint

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Background: Lung adenocarcinoma (LUAD) is the most common histological subtype of primary lung cancer. Thus, to figure out the biomarker of diagnosis for LUAD is of great significance. Long non-coding RNAs (lncRNAs) are previously revealed to exert vital effects in numerous cancers. LncRNA long intergenic non-protein coding RNA 520 (LINC00520) served as an oncogene in certain cancers. Therefore, our report was specially designed to probe role of LINC00520 in LUAD. Results: LINC00520 expression was detected by RT-qPCR. Next, function of LINC00520 in LUAD was verified by in vitro loss-of-function experiments. As for LINC00520 regulatory mechanism in LUAD, we conducted pull down, ChIP, RIP, and luciferase reporter assays. We found that LINC00520 was upregulated in LUAD. Additionally, LINC00520 upregulation suggested the poorer prognosis for patients with LUAD. Furthermore, LINC00520 downregulation suppressed LUAD cell proliferation and migration and induced cell apoptosis. Simultaneously, forkhead box P3 (FOXP3) is identified as the transcription factor (TF) to transcriptionally activate LINC00520. Moreover, LINC00520 positively upregulated FOXP3 via sponging miR-3611 in LUAD. Subsequently, rescue experiments delineated that miR-3611 downregulation or FOXP3 overexpression could reverse the effect of silenced LINC00520 on proliferative and migratory capabilities in LUAD. Conclusion: This study ﬁrst put forward and proved that lncRNA LINC00520 facilitated cell proliferative and migratory abilities in LUAD through interacting with miR-3611 and targeting FOXP3, which may provide a potential novel insight for treatment of LUAD.

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Transcription factor binding sites detection by using alignment-based approach

Cited by 4 publications

References 25 publications

Inferring gene correlation networks from transcription factor binding sites

Inferring gene correlation networks from transcription factor binding sites

RETRACTED ARTICLE: LncRNA LINC00520 aggravates cell proliferation and migration in lung adenocarcinoma via a positive feedback loop

LncRNA LINC00520 Aggravates Cell Proliferation and Migration in Lung Adenocarcinoma via a Positive Feedback Loop

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