A linear model for transcription factor binding affinity prediction in protein binding microarrays.
A linear model for transcription factor binding affinity prediction in protein binding microarrays.
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DOI：
10.1371/journal.pone.0020059
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Nykter M
中科院分区：
文献类型：
--
作者：
Annala M;Laurila K;Lähdesmäki H;Nykter M
蛋白质结合微阵列（PBM）是一种用于表征蛋白质-DNA结合的高通量技术。这些阵列可以同时测量蛋白质对数千个双链DNA序列的亲和力，从而产生一个全面的结合特异性目录。我们提出了一个线性模型预测的结合亲和力的蛋白质对DNA序列的PBM数据的基础上。我们的模型代表了一个单独的探针的结合亲和力贡献的探针的双折射的总和测量的强度。这些双链特征的DNA结合基序，并可用于预测对任意DNA序列的蛋白质结合的强度。我们的方法在逆向工程评估和方法对话5（DREAM 5）转录因子/DNA基序识别挑战中表现最好。对于DREAM 5奖励挑战，我们还开发了一种基于PBM结合谱鉴定转录因子的方法。我们的TF识别方法在奖金挑战中取得了最好的成绩。
Protein binding microarrays (PBM) are a high throughput technology used to characterize protein-DNA binding. The arrays measure a protein's affinity toward thousands of double-stranded DNA sequences at once, producing a comprehensive binding specificity catalog. We present a linear model for predicting the binding affinity of a protein toward DNA sequences based on PBM data. Our model represents the measured intensity of an individual probe as a sum of the binding affinity contributions of the probe's subsequences. These subsequences characterize a DNA binding motif and can be used to predict the intensity of protein binding against arbitrary DNA sequences. Our method was the best performer in the Dialogue for Reverse Engineering Assessments and Methods 5 (DREAM5) transcription factor/DNA motif recognition challenge. For the DREAM5 bonus challenge, we also developed an approach for the identification of transcription factors based on their PBM binding profiles. Our approach for TF identification achieved the best performance in the bonus challenge.
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影响因子：
4.3
作者：
Beyer A;Workman C;Hollunder J;Radke D;Möller U;Wilhelm T;Ideker T
通讯作者：
Ideker T
影响因子：
14.8
作者：
Berger, Michael F.;Bulyk, Martha L.
通讯作者：
Bulyk, Martha L.
DOI：
10.1126/science.1183621
发表时间：
2010-04-09
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Kasowski M;Grubert F;Heffelfinger C;Hariharan M;Asabere A;Waszak SM;Habegger L;Rozowsky J;Shi M;Urban AE;Hong MY;Karczewski KJ;Huber W;Weissman SM;Gerstein MB;Korbel JO;Snyder M
通讯作者：
Snyder M
影响因子：
4.3
作者：
Agius, Phaedra;Arvey, Aaron;Leslie, Christina
通讯作者：
Leslie, Christina
影响因子：
46.9
作者：
Berger, Michael F.;Philippakis, Anthony A.;Bulyk, Martha L.
通讯作者：
Bulyk, Martha L.