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ROS-DET: robust detector of switching mechanisms in gene expression.

Kayano M, Takigawa I, Shiga M, Tsuda K, Mamitsuka H - Nucleic Acids Res. (2011)

Bottom Line: Furthermore, for each of the top 10 pairs ranked by ROS-DET, we attempted to identify a pathway, i.e. consecutive biological phenomena, being related with the corresponding two genes by checking the biological literature.In 8 out of the 10 pairs, we found two parallel pathways, one of the two genes being in each of the two pathways and two pathways coming to (or starting with) the same gene.This indicates that two parallel pathways would be cooperatively used under one experimental condition, corresponding to the positive correlation, and the two pathways might be alternatively used under the other condition, corresponding to the negative correlation.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji 611-0011, Japan.

ABSTRACT
A switching mechanism in gene expression, where two genes are positively correlated in one condition and negatively correlated in the other condition, is a key to elucidating complex biological systems. There already exist methods for detecting switching mechanisms from microarrays. However, current approaches have problems under three real cases: outliers, expression values with a very small range and a small number of examples. ROS-DET overcomes these three problems, keeping the computational complexity of current approaches. We demonstrated that ROS-DET outperformed existing methods, under that all these three situations are considered. Furthermore, for each of the top 10 pairs ranked by ROS-DET, we attempted to identify a pathway, i.e. consecutive biological phenomena, being related with the corresponding two genes by checking the biological literature. In 8 out of the 10 pairs, we found two parallel pathways, one of the two genes being in each of the two pathways and two pathways coming to (or starting with) the same gene. This indicates that two parallel pathways would be cooperatively used under one experimental condition, corresponding to the positive correlation, and the two pathways might be alternatively used under the other condition, corresponding to the negative correlation. ROS-DET is available from http://www.bic.kyoto-u.ac.jp/pathway/kayano/ros-det.htm.

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Pathways for the fourth ranked gene pair.
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Figure 12: Pathways for the fourth ranked gene pair.

Mentions: HS3ST1 is one of two genes appeared in the second ranked gene pair. We can then use the same pathway as that of the second ranked gene pair, and TP53 binds to a TBP and represses its transcription (43). On the other hand, TAF10 forms a complex (TFIID) with TBP and other proteins, where TAF10 is important for stabilizing the complex. We can summarize these genes into Figure 12, which shows that HS3ST1 and TAF10 have parallel pathways. Two conditions of GDS2545 are normal tissues and prostate tumor tissues. Figure 6d shows that TAF10 and HS3ST1 are negatively correlated in expression under normal tissues, while they are positively correlated under tumor tissues. Here, TP53 represses transcription of TBP, with which TAF10 forms a complex, by which under normal tissues, the negative correlation between TAF10 and HS3ST1 might indicate that both TBP and TAF10 are highly expressed when the expression of HS3ST1 is low (and that of TAF10 is high), while they are both not expressed highly when the expression of HS3ST1 is high (and that of TAF10 is low). On the other hand, under tumor tissues, the expression of TAF10 and that of HS3ST1 are positively correlated, possibly implying that the balance in expression between TBP and TAF10 (by which a complex will be formed) is not kept well, maybe because of the disorder, i.e. prostate tumor. In order to confirm this inference, we checked the biweight midcorrelation between TBP and two genes in the fourth ranked pair, and Table 4 shows the result. From this table, we can see that under normal tissues, TBP is negatively and positively correlated with HS3ST1 and TAF10, respectively, being consistent with our scenario. That is, under normal tissues, both TBP and TAF10 can be expressed highly when HS3ST1 is not expressed well, while TBP and TAF10 will not be expressed highly if HS3ST1 is expressed well. On the other hand, under tumor tissues, TBP can be positively correlated with both HS3ST1 and TAF10, although the correlation values are relatively slight, indicating that the above scenario or mechanism under normal tissues would not work well under tumor tissues.Figure 12.


ROS-DET: robust detector of switching mechanisms in gene expression.

Kayano M, Takigawa I, Shiga M, Tsuda K, Mamitsuka H - Nucleic Acids Res. (2011)

Pathways for the fourth ranked gene pair.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3113587&req=5

Figure 12: Pathways for the fourth ranked gene pair.
Mentions: HS3ST1 is one of two genes appeared in the second ranked gene pair. We can then use the same pathway as that of the second ranked gene pair, and TP53 binds to a TBP and represses its transcription (43). On the other hand, TAF10 forms a complex (TFIID) with TBP and other proteins, where TAF10 is important for stabilizing the complex. We can summarize these genes into Figure 12, which shows that HS3ST1 and TAF10 have parallel pathways. Two conditions of GDS2545 are normal tissues and prostate tumor tissues. Figure 6d shows that TAF10 and HS3ST1 are negatively correlated in expression under normal tissues, while they are positively correlated under tumor tissues. Here, TP53 represses transcription of TBP, with which TAF10 forms a complex, by which under normal tissues, the negative correlation between TAF10 and HS3ST1 might indicate that both TBP and TAF10 are highly expressed when the expression of HS3ST1 is low (and that of TAF10 is high), while they are both not expressed highly when the expression of HS3ST1 is high (and that of TAF10 is low). On the other hand, under tumor tissues, the expression of TAF10 and that of HS3ST1 are positively correlated, possibly implying that the balance in expression between TBP and TAF10 (by which a complex will be formed) is not kept well, maybe because of the disorder, i.e. prostate tumor. In order to confirm this inference, we checked the biweight midcorrelation between TBP and two genes in the fourth ranked pair, and Table 4 shows the result. From this table, we can see that under normal tissues, TBP is negatively and positively correlated with HS3ST1 and TAF10, respectively, being consistent with our scenario. That is, under normal tissues, both TBP and TAF10 can be expressed highly when HS3ST1 is not expressed well, while TBP and TAF10 will not be expressed highly if HS3ST1 is expressed well. On the other hand, under tumor tissues, TBP can be positively correlated with both HS3ST1 and TAF10, although the correlation values are relatively slight, indicating that the above scenario or mechanism under normal tissues would not work well under tumor tissues.Figure 12.

Bottom Line: Furthermore, for each of the top 10 pairs ranked by ROS-DET, we attempted to identify a pathway, i.e. consecutive biological phenomena, being related with the corresponding two genes by checking the biological literature.In 8 out of the 10 pairs, we found two parallel pathways, one of the two genes being in each of the two pathways and two pathways coming to (or starting with) the same gene.This indicates that two parallel pathways would be cooperatively used under one experimental condition, corresponding to the positive correlation, and the two pathways might be alternatively used under the other condition, corresponding to the negative correlation.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji 611-0011, Japan.

ABSTRACT
A switching mechanism in gene expression, where two genes are positively correlated in one condition and negatively correlated in the other condition, is a key to elucidating complex biological systems. There already exist methods for detecting switching mechanisms from microarrays. However, current approaches have problems under three real cases: outliers, expression values with a very small range and a small number of examples. ROS-DET overcomes these three problems, keeping the computational complexity of current approaches. We demonstrated that ROS-DET outperformed existing methods, under that all these three situations are considered. Furthermore, for each of the top 10 pairs ranked by ROS-DET, we attempted to identify a pathway, i.e. consecutive biological phenomena, being related with the corresponding two genes by checking the biological literature. In 8 out of the 10 pairs, we found two parallel pathways, one of the two genes being in each of the two pathways and two pathways coming to (or starting with) the same gene. This indicates that two parallel pathways would be cooperatively used under one experimental condition, corresponding to the positive correlation, and the two pathways might be alternatively used under the other condition, corresponding to the negative correlation. ROS-DET is available from http://www.bic.kyoto-u.ac.jp/pathway/kayano/ros-det.htm.

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