P184
P185
Comparative functional genomics of the fission yeastsFull-length transcriptome assembly from RNA-Seq data without a reference genomeAb initio prediction of transcription factor targets using structural knowledge.Single-nucleosome mapping of histone modifications in S. cerevisiaeEpigenomics and the structure of the living genomeClk post-transcriptional control denoises circadian transcription both temporally and spatially.Sfp1 is a stress- and nutrient-sensitive regulator of ribosomal protein gene expression.High-resolution view of the yeast meiotic program revealed by ribosome profiling.De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysisUsing Bayesian networks to analyze expression dataContext-specific Bayesian clustering for gene expression data.Inferring quantitative models of regulatory networks from expression data.Nucleosome positioning from tiling microarray data.A novel Bayesian DNA motif comparison method for clustering and retrievalCell cycle- and chaperone-mediated regulation of H3K56ac incorporation in yeast.Ab initio construction of a eukaryotic transcriptome by massively parallel mRNA sequencing.Identifying novel constrained elements by exploiting biased substitution patternsReplication and active demethylation represent partially overlapping mechanisms for erasure of H3K4me3 in budding yeastModularity and directionality in genetic interaction maps.High throughput determination of TGFβ1/SMAD3 targets in A549 lung epithelial cellsAn integrative clustering and modeling algorithm for dynamical gene expression data.RNA polymerase mapping during stress responses reveals widespread nonproductive transcription in yeast.Strand-specific RNA sequencing reveals extensive regulated long antisense transcripts that are conserved across yeast species.Inferring cellular networks using probabilistic graphical models.A module map showing conditional activity of expression modules in cancer.Systematic dissection of roles for chromatin regulators in a yeast stress response.Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens.Paternally induced transgenerational environmental reprogramming of metabolic gene expression in mammals.High-resolution sequencing and modeling identifies distinct dynamic RNA regulatory strategiesExploring transcription regulation through cell-to-cell variability.Single-cell RNA-seq reveals dynamic paracrine control of cellular variation.Immunogenetics. Chromatin state dynamics during blood formationMapping Nucleosome Resolution Chromosome Folding in Yeast by Micro-CFrom signatures to models: understanding cancer using microarrays.Condition-specific genetic interaction maps reveal crosstalk between the cAMP/PKA and the HOG MAPK pathways in the activation of the general stress responseChromatin Dynamics and the RNA Exosome Function in Concert to Regulate Transcriptional Homeostasis.From large-scale assays to mechanistic insights: computational analysis of interactions.High-resolution chromatin dynamics during a yeast stress response.Blood transcriptional signatures of multiple sclerosis: unique gene expression of disease activity.Elucidating Combinatorial Chromatin States at Single-Nucleosome Resolution.
P50
Q22065619-A1943A9F-A867-4B15-A5C1-C1F1AF0BE5D1Q24620766-9308EBBA-C7C2-4825-870A-25A49A449215Q24811418-4044EC1E-52D2-4BBF-941D-FD55AE731440Q24816848-324C85D9-0168-4F2F-A251-7743266AD81EQ26782740-D3D162FB-F83B-4F79-8030-5B7B780CF7A4Q27324401-57B930FE-A3E9-478C-9274-38E517271BC5Q27931583-0ABCCEC4-9A09-41BB-8229-73068DEB4B3DQ27934600-2FF30F66-E88E-4EA8-A48B-C4BE6D0A2D08Q29615950-3C2BDEBB-94AC-4FF3-AE3D-830F29E49C26Q29617295-B5AEB2EF-FD40-461E-B95A-F92C8E44FAE0Q30693037-1960295C-EC95-445C-8A3B-48E8D90F7279Q30945362-31B1C82A-447A-4380-9A10-BF0F3C0F3556Q31160816-B6ADA1FF-53E3-4F27-A747-710F1D0AAFD0Q33332464-56EDB66B-14DB-45EA-912B-07DADDB4A6F6Q33386002-DDF9457A-8DE0-4801-AB66-D2EFE61BE558Q33408115-DE932101-F974-400A-853D-7C1BB9015EECQ33455323-8351C0E7-55F1-49E4-A54A-5DEAF581DCC4Q33530082-9DF86F75-0713-4720-98F5-8E96A322F7F7Q33896263-72C47FE3-5AE6-43A3-A2FF-9EDAE6F059B4Q33916249-2A2EBB0B-769A-443A-95CA-FBDE6F07F0A9Q33936107-AF6E2E79-823D-4262-8BFD-26DE7414D4F9Q34080284-B36A2795-D6DB-484B-8396-B9CC8B435D20Q34156146-0420A2BE-AD48-42EF-A5E4-BAA63BEEA99BQ34295031-B1BB98BF-C849-4ADD-A64F-308685336E7DQ34353143-8E6AD8B6-DBE4-45AF-A844-02BAA9055F89Q34388397-E7F2810D-E63E-45CB-8BAA-6783FD872DE2Q34547509-116F36EE-3E12-4CC9-B9E5-E337F87869B6Q34576185-91C78A15-1618-4681-AED3-DB13B9850C61Q34747008-AFEEE350-857C-4D09-833F-CCCEE0189EE1Q34794614-B8891A81-56AC-436F-AD43-9AF4A5FD71B3Q35185544-87B8C3EF-D296-440E-A742-816025DB0206Q35546671-F8A7EDCA-F9AA-4DA6-BA60-7BFFDEEBE3CCQ35871975-AF31F4C0-08E0-4B65-AD77-7838EA7B3F01Q36141647-BBA5548F-41A2-4335-B3B4-F7AF6A7A77B2Q36243717-7D5744BA-319F-40A3-B0E5-C98F048F44B3Q36327546-640EAC52-3C20-45CC-B71B-3515D7138C74Q37812512-D01BB0E2-56D2-486C-A296-2E99AC24F004Q39847543-22CEFE61-CF42-4288-89BD-6004BECB0401Q40528528-AAB186DE-30F9-418A-91C6-FF40B8D3672FQ41145370-3BC7065F-5088-4D6C-8B51-03F75BDFA692
P50
description
bioinformatician
@en
bioinformaticus
@nl
name
Nir Friedman
@ast
Nir Friedman
@de
Nir Friedman
@en
Nir Friedman
@es
Nir Friedman
@fr
Nir Friedman
@it
Nir Friedman
@nl
Nir Friedman
@sl
ניר פרידמן (מדען)
@he
type
label
Nir Friedman
@ast
Nir Friedman
@de
Nir Friedman
@en
Nir Friedman
@es
Nir Friedman
@fr
Nir Friedman
@it
Nir Friedman
@nl
Nir Friedman
@sl
ניר פרידמן (מדען)
@he
prefLabel
Nir Friedman
@ast
Nir Friedman
@de
Nir Friedman
@en
Nir Friedman
@es
Nir Friedman
@fr
Nir Friedman
@it
Nir Friedman
@nl
Nir Friedman
@sl
ניר פרידמן (מדען)
@he
P1006
P1015
P214
P227
P244
P269
P1006
P101
P1015
P1053
H-9692-2012
P1207
n2012169513
P184
P185
P21
P213
0000 0001 1471 8556