3. Cooper TA, Wan L, Dreyfuss G (2009) RNA
and disease. Cell 136:777–793
4. Darnell RB (2010) RNA regulation in neurologic disease and cancer. Cancer Res Treat
42:125–129
5. Cook KB, Kazan H, Zuberi K, Morris Q,
Hughes TR (2011) RBPDB: a database of
RNA-binding specificities. Nucleic Acids Res
39:D301–D308
6. Gerstberger S, Hafner M, Tuschl T (2014) A
census of human RNA-binding proteins. Nat
Rev Genet 15:829–845
7. Marondedze C, Thomas L, Serrano NL, Lilley
KS, Gehring L, Gehring S (2016) The
RNA-binding protein repertoire of Arabidopsis
thaliana. Sci Rep 6:29766
8. Stork C, Zheng S (2016) Genome-wide
profiling of RNA-protein interactions using
CLIP-seq. Methods Mol Biol 1421:137–151
9. Foley S, Gregory BD (2016) Protein interaction profile sequencing (PIP-seq). Curr Protoc
Mol Biol 116:27.5.1–27.5.15
10. Goasi SJ, Foley SW, Wang D, Silverman IM,
Selamoglu N, Nelson AD, Beilstein MA,
Daldal F, Deal RB, Gregory BD (2015) Global
analysis of the RNA-protein interaction and
RNA secondary structure landscapes of the
Arabidopsis nucleus. Mol Cell 57:376–388
11. Foley S, Gosai SJ, Wang D, Selamoglu N,
Solitti AC, Koster T, Steffen A, Lyons E,
Daldal F, Garcia BA, Staiger D, Deal RB, Gregory BD (2017) A global view of RNA-protein
interactions reveals novel root hair cell fate regulators. Dev Cell 41:204–220
12. Bailey TL, Elkan C (1994) Fitting a mixture
model by expectation maximization to discover
motifs in biopolymers. Proceedings of the
second international conference on intelligent
systems for molecular biology, pp 28–36
13. Heinz S, Brenner C, Spann N, Bertolino E, Lin
YC, Laslo P, Cheng JX, Murre C, Singh H,
Glass CK (2010) Simple combinations of
lineage-determining
transcription
factors
prime cis-regulatory elements required for
macrophage and B cell identities. Mol Cell
38:576–589
14. Smith CWJ (ed) (1998) RNA-protein interactions: a practical approach. Oxford University
Press, Oxford
15. Schwanh€ ausser B, Busse D, Dittmar G,
Schuchhardt J, Wolf J, Chen W, Selbach M
(2011) Global quantification of mammalian
gene expression control. Nature 473:337–342
16. Chahrour O, Cobice D, Malone J (2015) Stable isotope labelling methods in mass
spectrometry-based quantitative methods. J
Pharm Biomed Anal 113:2–20
17. Vidova V, Spacil Z (2017) A review on mass
spectrometry-based quantitative proteomics:
targeted and data independent acquisition.
Anal Chim Acta 964:7–23
18. Ray D, Kazan H, Cook KB, Weirauch MT,
Li X, Gueroussov S, Albu M, Zheng H,
Yang A, Na H, Irimia M, Matzat LH, Dale
RK, Smith SA, Yarosh CA, Kelly SM,
Nabet B, Mecenas D, Li W, Laishram RS,
Qiao M, Lipshitz HD, Piano F, Corbett AH,
Carstens RP, Frey BJ, Anderson RA, Lynch
KW, Penalva LOF, Lei EP, Fraser AG, Blencowe BJ, Morris QD, Hughes TR (2013) A
compendium of RNA-binding motifs for
decoding gene regulation. Nature 499
(7457):172–177
Using Bead-Tagged RNAs to Identify RBPs
253
and disease. Cell 136:777–793
4. Darnell RB (2010) RNA regulation in neurologic disease and cancer. Cancer Res Treat
42:125–129
5. Cook KB, Kazan H, Zuberi K, Morris Q,
Hughes TR (2011) RBPDB: a database of
RNA-binding specificities. Nucleic Acids Res
39:D301–D308
6. Gerstberger S, Hafner M, Tuschl T (2014) A
census of human RNA-binding proteins. Nat
Rev Genet 15:829–845
7. Marondedze C, Thomas L, Serrano NL, Lilley
KS, Gehring L, Gehring S (2016) The
RNA-binding protein repertoire of Arabidopsis
thaliana. Sci Rep 6:29766
8. Stork C, Zheng S (2016) Genome-wide
profiling of RNA-protein interactions using
CLIP-seq. Methods Mol Biol 1421:137–151
9. Foley S, Gregory BD (2016) Protein interaction profile sequencing (PIP-seq). Curr Protoc
Mol Biol 116:27.5.1–27.5.15
10. Goasi SJ, Foley SW, Wang D, Silverman IM,
Selamoglu N, Nelson AD, Beilstein MA,
Daldal F, Deal RB, Gregory BD (2015) Global
analysis of the RNA-protein interaction and
RNA secondary structure landscapes of the
Arabidopsis nucleus. Mol Cell 57:376–388
11. Foley S, Gosai SJ, Wang D, Selamoglu N,
Solitti AC, Koster T, Steffen A, Lyons E,
Daldal F, Garcia BA, Staiger D, Deal RB, Gregory BD (2017) A global view of RNA-protein
interactions reveals novel root hair cell fate regulators. Dev Cell 41:204–220
12. Bailey TL, Elkan C (1994) Fitting a mixture
model by expectation maximization to discover
motifs in biopolymers. Proceedings of the
second international conference on intelligent
systems for molecular biology, pp 28–36
13. Heinz S, Brenner C, Spann N, Bertolino E, Lin
YC, Laslo P, Cheng JX, Murre C, Singh H,
Glass CK (2010) Simple combinations of
lineage-determining
transcription
factors
prime cis-regulatory elements required for
macrophage and B cell identities. Mol Cell
38:576–589
14. Smith CWJ (ed) (1998) RNA-protein interactions: a practical approach. Oxford University
Press, Oxford
15. Schwanh€ ausser B, Busse D, Dittmar G,
Schuchhardt J, Wolf J, Chen W, Selbach M
(2011) Global quantification of mammalian
gene expression control. Nature 473:337–342
16. Chahrour O, Cobice D, Malone J (2015) Stable isotope labelling methods in mass
spectrometry-based quantitative methods. J
Pharm Biomed Anal 113:2–20
17. Vidova V, Spacil Z (2017) A review on mass
spectrometry-based quantitative proteomics:
targeted and data independent acquisition.
Anal Chim Acta 964:7–23
18. Ray D, Kazan H, Cook KB, Weirauch MT,
Li X, Gueroussov S, Albu M, Zheng H,
Yang A, Na H, Irimia M, Matzat LH, Dale
RK, Smith SA, Yarosh CA, Kelly SM,
Nabet B, Mecenas D, Li W, Laishram RS,
Qiao M, Lipshitz HD, Piano F, Corbett AH,
Carstens RP, Frey BJ, Anderson RA, Lynch
KW, Penalva LOF, Lei EP, Fraser AG, Blencowe BJ, Morris QD, Hughes TR (2013) A
compendium of RNA-binding motifs for
decoding gene regulation. Nature 499
(7457):172–177
Using Bead-Tagged RNAs to Identify RBPs
253