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Items: 1 to 50 of 114

1.

Neural field models for latent state inference: Application to large-scale neuronal recordings.

Rule ME, Schnoerr D, Hennig MH, Sanguinetti G.

PLoS Comput Biol. 2019 Nov 4;15(11):e1007442. doi: 10.1371/journal.pcbi.1007442. [Epub ahead of print]

2.

Geometric fluid approximation for general continuous-time Markov chains.

Michaelides M, Hillston J, Sanguinetti G.

Proc Math Phys Eng Sci. 2019 Sep;475(2229):20190100. doi: 10.1098/rspa.2019.0100. Epub 2019 Sep 25.

PMID:
31611711
3.

Melissa: Bayesian clustering and imputation of single-cell methylomes.

Kapourani CA, Sanguinetti G.

Genome Biol. 2019 Mar 21;20(1):61. doi: 10.1186/s13059-019-1665-8.

4.

Environmental Bacteria Involved in Manganese(II) Oxidation and Removal From Groundwater.

Piazza A, Ciancio Casalini L, Pacini VA, Sanguinetti G, Ottado J, Gottig N.

Front Microbiol. 2019 Feb 11;10:119. doi: 10.3389/fmicb.2019.00119. eCollection 2019.

5.

Using BRIE to Detect and Analyze Splicing Isoforms in scRNA-Seq Data.

Huang Y, Sanguinetti G.

Methods Mol Biol. 2019;1935:175-185. doi: 10.1007/978-1-4939-9057-3_12.

PMID:
30758827
6.

Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3.

Huynh-Thu VA, Sanguinetti G.

Methods Mol Biol. 2019;1883:217-233. doi: 10.1007/978-1-4939-8882-2_9.

7.

Gene Regulatory Network Inference: An Introductory Survey.

Huynh-Thu VA, Sanguinetti G.

Methods Mol Biol. 2019;1883:1-23. doi: 10.1007/978-1-4939-8882-2_1.

8.

Evolutionary dynamics of residual disease in human glioblastoma.

Spiteri I, Caravagna G, Cresswell GD, Vatsiou A, Nichol D, Acar A, Ermini L, Chkhaidze K, Werner B, Mair R, Brognaro E, Verhaak RGW, Sanguinetti G, Piccirillo SGM, Watts C, Sottoriva A.

Ann Oncol. 2019 Mar 1;30(3):456-463. doi: 10.1093/annonc/mdy506.

9.

Detecting repeated cancer evolution from multi-region tumor sequencing data.

Caravagna G, Giarratano Y, Ramazzotti D, Tomlinson I, Graham TA, Sanguinetti G, Sottoriva A.

Nat Methods. 2018 Sep;15(9):707-714. doi: 10.1038/s41592-018-0108-x. Epub 2018 Aug 31.

10.

Autoregressive Point Processes as Latent State-Space Models: A Moment-Closure Approach to Fluctuations and Autocorrelations.

Rule M, Sanguinetti G.

Neural Comput. 2018 Oct;30(10):2757-2780. doi: 10.1162/neco_a_01121. Epub 2018 Aug 27.

PMID:
30148704
11.

HuD Is a Neural Translation Enhancer Acting on mTORC1-Responsive Genes and Counteracted by the Y3 Small Non-coding RNA.

Tebaldi T, Zuccotti P, Peroni D, Köhn M, Gasperini L, Potrich V, Bonazza V, Dudnakova T, Rossi A, Sanguinetti G, Conti L, Macchi P, D'Agostino V, Viero G, Tollervey D, Hüttelmaier S, Quattrone A.

Mol Cell. 2018 Jul 19;71(2):256-270.e10. doi: 10.1016/j.molcel.2018.06.032.

12.

Corrigendum: Transcription rate strongly affects splicing fidelity and cotranscriptionality in budding yeast.

Aslanzadeh V, Huang Y, Sanguinetti G, Beggs JD.

Genome Res. 2018 Apr;28(4):606.2. doi: 10.1101/gr.236265.118. No abstract available.

13.

BPRMeth: a flexible Bioconductor package for modelling methylation profiles.

Kapourani CA, Sanguinetti G.

Bioinformatics. 2018 Jul 15;34(14):2485-2486. doi: 10.1093/bioinformatics/bty129.

14.

scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells.

Clark SJ, Argelaguet R, Kapourani CA, Stubbs TM, Lee HJ, Alda-Catalinas C, Krueger F, Sanguinetti G, Kelsey G, Marioni JC, Stegle O, Reik W.

Nat Commun. 2018 Feb 22;9(1):781. doi: 10.1038/s41467-018-03149-4.

15.

Transcription rate strongly affects splicing fidelity and cotranscriptionality in budding yeast.

Aslanzadeh V, Huang Y, Sanguinetti G, Beggs JD.

Genome Res. 2018 Feb;28(2):203-213. doi: 10.1101/gr.225615.117. Epub 2017 Dec 18. Erratum in: Genome Res. 2018 Apr;28(4):606.2.

16.

Efficient Low-Order Approximation of First-Passage Time Distributions.

Schnoerr D, Cseke B, Grima R, Sanguinetti G.

Phys Rev Lett. 2017 Nov 24;119(21):210601. doi: 10.1103/PhysRevLett.119.210601. Epub 2017 Nov 20.

PMID:
29219406
17.

The Broad-Spectrum Antimicrobial Potential of [Mn(CO)4(S2CNMe(CH2CO2H))], a Water-Soluble CO-Releasing Molecule (CORM-401): Intracellular Accumulation, Transcriptomic and Statistical Analyses, and Membrane Polarization.

Wareham LK, McLean S, Begg R, Rana N, Ali S, Kendall JJ, Sanguinetti G, Mann BE, Poole RK.

Antioxid Redox Signal. 2018 May 10;28(14):1286-1308. doi: 10.1089/ars.2017.7239. Epub 2017 Sep 28.

18.

Unbiased Bayesian inference for population Markov jump processes via random truncations.

Georgoulas A, Hillston J, Sanguinetti G.

Stat Comput. 2017;27(4):991-1002. doi: 10.1007/s11222-016-9667-9. Epub 2016 Jun 2.

19.

BRIE: transcriptome-wide splicing quantification in single cells.

Huang Y, Sanguinetti G.

Genome Biol. 2017 Jun 27;18(1):123. doi: 10.1186/s13059-017-1248-5.

20.

MeCP2 recognizes cytosine methylated tri-nucleotide and di-nucleotide sequences to tune transcription in the mammalian brain.

Lagger S, Connelly JC, Schweikert G, Webb S, Selfridge J, Ramsahoye BH, Yu M, He C, Sanguinetti G, Sowers LC, Walkinshaw MD, Bird A.

PLoS Genet. 2017 May 12;13(5):e1006793. doi: 10.1371/journal.pgen.1006793. eCollection 2017 May.

21.

Kinetic CRAC uncovers a role for Nab3 in determining gene expression profiles during stress.

van Nues R, Schweikert G, de Leau E, Selega A, Langford A, Franklin R, Iosub I, Wadsworth P, Sanguinetti G, Granneman S.

Nat Commun. 2017 Apr 11;8(1):12. doi: 10.1038/s41467-017-00025-5.

22.

DGW: an exploratory data analysis tool for clustering and visualisation of epigenomic marks.

Lukauskas S, Visintainer R, Sanguinetti G, Schweikert GB.

BMC Bioinformatics. 2016 Dec 13;17(Suppl 16):447. doi: 10.1186/s12859-016-1306-0.

23.

Trends and challenges in computational RNA biology.

Selega A, Sanguinetti G.

Genome Biol. 2016 Dec 7;17(1):253.

24.

Robust statistical modeling improves sensitivity of high-throughput RNA structure probing experiments.

Selega A, Sirocchi C, Iosub I, Granneman S, Sanguinetti G.

Nat Methods. 2017 Jan;14(1):83-89. doi: 10.1038/nmeth.4068. Epub 2016 Nov 7.

PMID:
27819660
25.

Higher order methylation features for clustering and prediction in epigenomic studies.

Kapourani CA, Sanguinetti G.

Bioinformatics. 2016 Sep 1;32(17):i405-i412. doi: 10.1093/bioinformatics/btw432.

PMID:
27587656
26.

Statistical modeling of isoform splicing dynamics from RNA-seq time series data.

Huang Y, Sanguinetti G.

Bioinformatics. 2016 Oct 1;32(19):2965-72. doi: 10.1093/bioinformatics/btw364. Epub 2016 Jun 17.

PMID:
27318208
27.

Strand-specific, high-resolution mapping of modified RNA polymerase II.

Milligan L, Huynh-Thu VA, Delan-Forino C, Tuck A, Petfalski E, Lombraña R, Sanguinetti G, Kudla G, Tollervey D.

Mol Syst Biol. 2016 Jun 10;12(6):874. doi: 10.15252/msb.20166869.

28.

Cox process representation and inference for stochastic reaction-diffusion processes.

Schnoerr D, Grima R, Sanguinetti G.

Nat Commun. 2016 May 25;7:11729. doi: 10.1038/ncomms11729.

29.

Network of epistatic interactions within a yeast snoRNA.

Puchta O, Cseke B, Czaja H, Tollervey D, Sanguinetti G, Kudla G.

Science. 2016 May 13;352(6287):840-4. doi: 10.1126/science.aaf0965. Epub 2016 Apr 14. Erratum in: Science. 2016 May 6;352(6286). pii: aaf9112. doi: 10.1126/science.aaf9112.

30.

In utero betamethasone affects 3β-hydroxysteroid dehydrogenase and inhibin-α immunoexpression during testis development.

Pedrana G, Viotti H, Lombide P, Sanguinetti G, Pino C, Cavestany D, Sloboda DM, Martin GB.

J Dev Orig Health Dis. 2016 Aug;7(4):342-9. doi: 10.1017/S2040174416000118. Epub 2016 Mar 29.

PMID:
27019950
31.

Carbon Monoxide Gas Is Not Inert, but Global, in Its Consequences for Bacterial Gene Expression, Iron Acquisition, and Antibiotic Resistance.

Wareham LK, Begg R, Jesse HE, Van Beilen JW, Ali S, Svistunenko D, McLean S, Hellingwerf KJ, Sanguinetti G, Poole RK.

Antioxid Redox Signal. 2016 Jun 10;24(17):1013-28. doi: 10.1089/ars.2015.6501. Epub 2016 Mar 30.

32.

CydDC-mediated reductant export in Escherichia coli controls the transcriptional wiring of energy metabolism and combats nitrosative stress.

Holyoake LV, Hunt S, Sanguinetti G, Cook GM, Howard MJ, Rowe ML, Poole RK, Shepherd M.

Biochem J. 2016 Mar 15;473(6):693-701. doi: 10.1042/BJ20150536. Epub 2015 Dec 23.

33.

Transcriptome-wide RNA processing kinetics revealed using extremely short 4tU labeling.

Barrass JD, Reid JE, Huang Y, Hector RD, Sanguinetti G, Beggs JD, Granneman S.

Genome Biol. 2015 Dec 17;16:282. doi: 10.1186/s13059-015-0848-1.

34.

Comparison of different moment-closure approximations for stochastic chemical kinetics.

Schnoerr D, Sanguinetti G, Grima R.

J Chem Phys. 2015 Nov 14;143(18):185101. doi: 10.1063/1.4934990.

35.

Improving Fiber Alignment in HARDI by Combining Contextual PDE Flow with Constrained Spherical Deconvolution.

Portegies JM, Fick RH, Sanguinetti GR, Meesters SP, Girard G, Duits R.

PLoS One. 2015 Oct 14;10(10):e0138122. doi: 10.1371/journal.pone.0138122. eCollection 2015.

36.

Analysis of transcript changes in a heme-deficient mutant of Escherichia coli in response to CORM-3 [Ru(CO)3Cl(glycinate)].

Wilson JL, McLean S, Begg R, Sanguinetti G, Poole RK.

Genom Data. 2015 Jun 13;5:231-234. eCollection 2015 Sep.

37.

RiboAbacus: a model trained on polyribosome images predicts ribosome density and translational efficiency from mammalian transcriptomes.

Lauria F, Tebaldi T, Lunelli L, Struffi P, Gatto P, Pugliese A, Brigotti M, Montanaro L, Ciribilli Y, Inga A, Quattrone A, Sanguinetti G, Viero G.

Nucleic Acids Res. 2015 Dec 15;43(22):e153. doi: 10.1093/nar/gkv781. Epub 2015 Aug 3.

38.

A Bayesian approach for structure learning in oscillating regulatory networks.

Trejo Banos D, Millar AJ, Sanguinetti G.

Bioinformatics. 2015 Nov 15;31(22):3617-24. doi: 10.1093/bioinformatics/btv414. Epub 2015 Jul 14.

39.

CO-Releasing Molecules Have Nonheme Targets in Bacteria: Transcriptomic, Mathematical Modeling and Biochemical Analyses of CORM-3 [Ru(CO)3Cl(glycinate)] Actions on a Heme-Deficient Mutant of Escherichia coli.

Wilson JL, Wareham LK, McLean S, Begg R, Greaves S, Mann BE, Sanguinetti G, Poole RK.

Antioxid Redox Signal. 2015 Jul 10;23(2):148-62. doi: 10.1089/ars.2014.6151. Epub 2015 Apr 28.

40.

Combining tree-based and dynamical systems for the inference of gene regulatory networks.

Huynh-Thu VA, Sanguinetti G.

Bioinformatics. 2015 May 15;31(10):1614-22. doi: 10.1093/bioinformatics/btu863. Epub 2015 Jan 7.

41.

Adaptation of anaerobic cultures of Escherichia coli K-12 in response to environmental trimethylamine-N-oxide.

Denby KJ, Rolfe MD, Crick E, Sanguinetti G, Poole RK, Green J.

Environ Microbiol. 2015 Jul;17(7):2477-91. doi: 10.1111/1462-2920.12726. Epub 2015 Feb 3.

42.

M3D: a kernel-based test for spatially correlated changes in methylation profiles.

Mayo TR, Schweikert G, Sanguinetti G.

Bioinformatics. 2015 Mar 15;31(6):809-16. doi: 10.1093/bioinformatics/btu749. Epub 2014 Nov 13.

43.

Transcription factor binding predicts histone modifications in human cell lines.

Benveniste D, Sonntag HJ, Sanguinetti G, Sproul D.

Proc Natl Acad Sci U S A. 2014 Sep 16;111(37):13367-72. doi: 10.1073/pnas.1412081111. Epub 2014 Sep 3.

44.

Validity conditions for moment closure approximations in stochastic chemical kinetics.

Schnoerr D, Sanguinetti G, Grima R.

J Chem Phys. 2014 Aug 28;141(8):084103. doi: 10.1063/1.4892838.

PMID:
25173001
45.

The complex chemical Langevin equation.

Schnoerr D, Sanguinetti G, Grima R.

J Chem Phys. 2014 Jul 14;141(2):024103. doi: 10.1063/1.4885345.

PMID:
25027995
46.

Semisupervised multitask learning with Gaussian processes.

Skolidis G, Sanguinetti G.

IEEE Trans Neural Netw Learn Syst. 2013 Dec;24(12):2101-12. doi: 10.1109/TNNLS.2013.2272403.

PMID:
24805226
47.

Towards a systems level understanding of the oxygen response of Escherichia coli.

Bettenbrock K, Bai H, Ederer M, Green J, Hellingwerf KJ, Holcombe M, Kunz S, Rolfe MD, Sanguinetti G, Sawodny O, Sharma P, Steinsiek S, Poole RK.

Adv Microb Physiol. 2014;64:65-114. doi: 10.1016/B978-0-12-800143-1.00002-6. Review.

PMID:
24797925
48.

Single-trial classification of EEG in a visual object task using ICA and machine learning.

Stewart AX, Nuthmann A, Sanguinetti G.

J Neurosci Methods. 2014 May 15;228:1-14. doi: 10.1016/j.jneumeth.2014.02.014. Epub 2014 Mar 5.

49.

MMDiff: quantitative testing for shape changes in ChIP-Seq data sets.

Schweikert G, Cseke B, Clouaire T, Bird A, Sanguinetti G.

BMC Genomics. 2013 Nov 24;14:826. doi: 10.1186/1471-2164-14-826.

50.

Simultaneous inference and clustering of transcriptional dynamics in gene regulatory networks.

Asif HM, Sanguinetti G.

Stat Appl Genet Mol Biol. 2013 Oct 1;12(5):545-57. doi: 10.1515/sagmb-2012-0010.

PMID:
24051920

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