CoChat
Sign Up
Cited by 113

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

A comprehensive joint analysis of the long and short RNA transcriptomes of human erythrocytes.

Doss, Jennifer F., Corcoran, David L., Jima, Dereje D., Telen, Marilyn J., Dave, Sandeep S., Chi, Jen-Tsan·2015·BMC Genomics
DOI 10.1186/s12864-015-2156-2PMC PMC4647483
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s12864-015-2156-2

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Population characteristics of erythrocyte long RNAs. a Distribution of total number of expressed transcripts across indicated cell types. A transcript was considered expressed if the RPKM value was ≥0.5. b GSEA analysis of the top 500 expressed erythrocyte transcripts in the ranked expressed transcripts of the day 8 erythroid progenitor (D8) vs. PBMC samples. c Distribution of the genomic location for RNA-Seq reads. Locations represent average number of reads for the three RBC, three PBMC, and two erythroid progenitor independent samples. Intronic reads are included if <10 kb upstream of a transcription start site or <10 kb downstream of a transcription end site
Fig. 1 Population characteristics of erythrocyte long RNAs. a Distribution of total number of expressed transcripts across indicated cell types. A transcript was considered expressed if the RPKM value was ≥0.5. b GSEA analysis of the top 500 expressed erythrocyte transcripts in the ranked expressed transcripts of the day 8 erythroid progenitor (D8) vs. PBMC samples. c Distribution of the genomic location for RNA-Seq reads. Locations represent average number of reads for the three RBC, three PBMC, and two erythroid progenitor independent samples. Intronic reads are included if <10 kb upstream of a transcription start site or <10 kb downstream of a transcription end siteCC BY · PubMed Central
Fig. 2 MicroRNA identification pipeline and microRNA population characteristics. a Deep sequencing of short RNAs from mature RBCs was performed using the Illumina HiSeq technology. Raw sequences were mapped to the genome, filtered through miRDeep, and categorized. Identified microRNA loci were cross-referenced with miRBase (v. 21) and UCSC Genome Browser (hg 38) to distinguish between candidate novel and known microRNAs. b Relative percentages (log base 10) of read numbers for top 10 microRNAs among five different samples. Percentages are averages based on total number of known, mature microRNA reads for each sample. c Relative percentage representation of genomic location for both known and putative microRNAs. Listed genomic locations for mature microRNA sequences are relative to RefSeq annotated transcripts in UCSC genome browser (hg38). For mature microRNA sequences spanning both introns and coding region, or UTR and coding regions of alternative transcripts, sequence is listed in coding region. d Relative percentage representation of sequence conservation for both known and putative microRNAs. Conservation of mature microRNA sequence indicates exact sequence alignment in listed number of species (from human, chimp, rhesus monkey, dog, mouse, and zebrafish), allowing for up to one base mismatch outside of the microRNA seed sequence (nucleotides 2–8), and no mismatches within the seed sequence
Fig. 2 MicroRNA identification pipeline and microRNA population characteristics. a Deep sequencing of short RNAs from mature RBCs was performed using the Illumina HiSeq technology. Raw sequences were mapped to the genome, filtered through miRDeep, and categorized. Identified microRNA loci were cross-referenced with miRBase (v. 21) and UCSC Genome Browser (hg 38) to distinguish between candidate novel and known microRNAs. b Relative percentages (log base 10) of read numbers for top 10 microRNAs among five different samples. Percentages are averages based on total number of known, mature microRNA reads for each sample. c Relative percentage representation of genomic location for both known and putative microRNAs. Listed genomic locations for mature microRNA sequences are relative to RefSeq annotated transcripts in UCSC genome browser (hg38). For mature microRNA sequences spanning both introns and coding region, or UTR and coding regions of alternative transcripts, sequence is listed in coding region. d Relative percentage representation of sequence conservation for both known and putative microRNAs. Conservation of mature microRNA sequence indicates exact sequence alignment in listed number of species (from human, chimp, rhesus monkey, dog, mouse, and zebrafish), allowing for up to one base mismatch outside of the microRNA seed sequence (nucleotides 2–8), and no mismatches within the seed sequenceCC BY · PubMed Central
Fig. 3 Genomic locations and expression dynamics of noncoding RNAs in the miR-144/451 locus. a Location, sequence read length, relative read number, and vertebrate conservation of noncoding RNAs within the chromosome 17 miR-144/451 locus according to UCSC genome browser (hg38). Solid lines indicate average read numbers, dotted lines represent highest and lowest standard deviations of read numbers across the samples. The strand orientation of each noncoding RNA is indicated. b Expression of miR-144-5p, miR-4732-3p, and miR-4732-5p, during CD34+ erythroid differentiation using progenitors from three different individuals. Differentiation is listed from day 6 to day 16. U6 snRNA was used as a loading control, with fold difference set relative to day 6. c Predicted folding structure for the miR-4732 pre-microRNA according to miRDeep, and frequency of reads for mature microRNAs from one representative sample. The most prevalent mature read sequences for miR-4732-3p (red) and miR-4732-5p (blue) are shown. d Sequence conservation of miR-4732-3p in listed species. Primates are listed adjacent to white box, non-primates listed adjacent to black box. Seed sequence is boxed, and base mismatches with human miR-4732-3p are highlighted in red
Fig. 3 Genomic locations and expression dynamics of noncoding RNAs in the miR-144/451 locus. a Location, sequence read length, relative read number, and vertebrate conservation of noncoding RNAs within the chromosome 17 miR-144/451 locus according to UCSC genome browser (hg38). Solid lines indicate average read numbers, dotted lines represent highest and lowest standard deviations of read numbers across the samples. The strand orientation of each noncoding RNA is indicated. b Expression of miR-144-5p, miR-4732-3p, and miR-4732-5p, during CD34+ erythroid differentiation using progenitors from three different individuals. Differentiation is listed from day 6 to day 16. U6 snRNA was used as a loading control, with fold difference set relative to day 6. c Predicted folding structure for the miR-4732 pre-microRNA according to miRDeep, and frequency of reads for mature microRNAs from one representative sample. The most prevalent mature read sequences for miR-4732-3p (red) and miR-4732-5p (blue) are shown. d Sequence conservation of miR-4732-3p in listed species. Primates are listed adjacent to white box, non-primates listed adjacent to black box. Seed sequence is boxed, and base mismatches with human miR-4732-3p are highlighted in redCC BY · PubMed Central
Fig. 4 MiR-4732-3p directly regulates SMAD2 and SMAD4. a Alignment of miR-4732-3p and predicted target sites in the 3’ UTR of SMAD2 and SMAD4, with mutated binding sites underlined. b Relative changes of reporter activities in K562s co-transfected with luciferase constructs and indicated microRNA mimics. A portion the SMAD2 and the SMAD4 3’UTRs were cloned downstream of Renilla in separate psiCheck-2 vectors (n = 4) (*p < 0.05, **p < 0.01). c Relative changes of reporter activities in K562s co-transfected with luciferase constructs and microRNA-blocking antisense oligonucleotides (ASOs) (n = 4) (***p < 0.001). d Relative changes of reporter activities in K562s transfected with wild-type (WT) or mutated (MUT) SMAD2 or SMAD4 3’UTR constructs (n = 4) (*p < 0.05). e Western blots of SMAD2 and SMAD4 protein expression 24 h after transfection of K562s with indicated microRNA mimics or f microRNA ASOs. α-tubulin was used as a loading control for normalization. Protein desitometric values are listed. The fold change of expression for each treatment group was first normalized to alpha tubulin input for that treatment, then ratio set relative to the control treatment
Fig. 4 MiR-4732-3p directly regulates SMAD2 and SMAD4. a Alignment of miR-4732-3p and predicted target sites in the 3’ UTR of SMAD2 and SMAD4, with mutated binding sites underlined. b Relative changes of reporter activities in K562s co-transfected with luciferase constructs and indicated microRNA mimics. A portion the SMAD2 and the SMAD4 3’UTRs were cloned downstream of Renilla in separate psiCheck-2 vectors (n = 4) (*p < 0.05, **p < 0.01). c Relative changes of reporter activities in K562s co-transfected with luciferase constructs and microRNA-blocking antisense oligonucleotides (ASOs) (n = 4) (***p < 0.001). d Relative changes of reporter activities in K562s transfected with wild-type (WT) or mutated (MUT) SMAD2 or SMAD4 3’UTR constructs (n = 4) (*p < 0.05). e Western blots of SMAD2 and SMAD4 protein expression 24 h after transfection of K562s with indicated microRNA mimics or f microRNA ASOs. α-tubulin was used as a loading control for normalization. Protein desitometric values are listed. The fold change of expression for each treatment group was first normalized to alpha tubulin input for that treatment, then ratio set relative to the control treatmentCC BY · PubMed Central
Fig. 5 MiR-4732-3p regulates TGF-β signaling and promotes erythroid cell proliferation. a Relative changes of TGF-β reporter activities in K562 cells co-transfected with luciferase constructs and microRNA mimics (n = 4) (**p < 0.01). Cells were transfected with microRNA mimic, SMAD4-responsive Firefly construct, and a separate Renilla construct as a transfection control. b Relative mRNA levels of p21, PAI-1, Bax, and Bim 24 h after transfection of K562s with microRNA mimics. Levels were determined using qPCR and normalized by GAPDH (n = 3) (*p < 0.05). c Total cell number of primary CD34 erythroid progenitors 48 and 72 h after equal numbers of cells were transfected with indicated microRNA mimics on day 8 of differentiation (n = 3) (*p < 0.05). d Total cell number of primary CD34 erythroid progenitors 24 h after equal numbers of cells were transfected with indicated combinations of microRNA mimics and overexpression constructs on day 8 of differentiation (n = 3) (*p < 0.05, **p < 0.01)
Fig. 5 MiR-4732-3p regulates TGF-β signaling and promotes erythroid cell proliferation. a Relative changes of TGF-β reporter activities in K562 cells co-transfected with luciferase constructs and microRNA mimics (n = 4) (**p < 0.01). Cells were transfected with microRNA mimic, SMAD4-responsive Firefly construct, and a separate Renilla construct as a transfection control. b Relative mRNA levels of p21, PAI-1, Bax, and Bim 24 h after transfection of K562s with microRNA mimics. Levels were determined using qPCR and normalized by GAPDH (n = 3) (*p < 0.05). c Total cell number of primary CD34 erythroid progenitors 48 and 72 h after equal numbers of cells were transfected with indicated microRNA mimics on day 8 of differentiation (n = 3) (*p < 0.05). d Total cell number of primary CD34 erythroid progenitors 24 h after equal numbers of cells were transfected with indicated combinations of microRNA mimics and overexpression constructs on day 8 of differentiation (n = 3) (*p < 0.05, **p < 0.01)CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 3 references

Research works printed but not linked: 62

  1. Zhao G, Yu D, Weiss MJ. MicroRNAs in erythropoiesis. Curr Opin Hematol. 2010;17:155–62.
  2. Hamilton AJ. MicroRNA in erythrocytes. Biochem Soc Trans. 2010;38:229–31. · DOI · Paper page
  3. O’Brien CA, Harley JB. A subset of hY RNAs is associated with erythrocyte Ro ribonucleoproteins. EMBO J. 1990;9:3683–9. · DOI
  4. Chen SY, Wang Y, Telen MJ, Chi JT. The genomic analysis of erythrocyte microRNA expression in sickle cell diseases. PLoS One. 2008;3:e2360. · DOI · Paper page
  5. Rathjen T, Nicol C, McConkey G, Dalmay T. Analysis of short RNAs in the malaria parasite and its red blood cell host. FEBS Lett. 2006;580:5185–8. · DOI · Paper page
  6. LaMonte G, Philip N, Reardon J, Lacsina JR, Majoros W, Chapman L, et al. Translocation of sickle cell erythrocyte microRNAs into Plasmodium falciparum inhibits parasite translation and contributes to malaria resistance. Cell Host Microbe. 2012;12:187–99. · DOI · Paper page
  7. Sangokoya C, Telen MJ, Chi JT. microRNA miR-144 modulates oxidative stress tolerance and associates with anemia severity in sickle cell disease. Blood. 2010;116:4338–48. · DOI
  8. Dore LC, Amigo JD, Dos Santos CO, Zhang Z, Gai X, Tobias JW, et al. A GATA-1-regulated microRNA locus essential for erythropoiesis. Proc Natl Acad Sci U S A. 2008;105:3333–8. · DOI · Paper page
  9. Kannan M, Atreya C. Differential profiling of human red blood cells during storage for 52 selected microRNAs. Transfusion. 2010;50:1581–8. · DOI · Paper page
  10. Azzouzi I, Moest H, Wollscheid B, Schmugge M, Eekels JJ, Speer O. Deep Sequencing and Proteomic Analysis of the MicroRNA-Induced Silencing Complex in Human Red Blood Cells. Exp Hematol. 2015;43(5):382–92. · DOI · Paper page
  11. Lewis BP, Burge CB, Bartel DP. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005;120:15–20. · DOI · Paper page
  12. He W, Dorn DC, Erdjument-Bromage H, Tempst P, Moore MA, Massague J. Hematopoiesis controlled by distinct TIF1gamma and Smad4 branches of the TGFbeta pathway. Cell. 2006;125:929–41. · DOI
  13. Sangokoya C, LaMonte G, Chi JT. Isolation and characterization of microRNAs of human mature erythrocytes. Methods Mol Biol. 2010;667:193–203. · DOI
  14. Wong JJ, Ritchie W, Ebner OA, Selbach M, Wong JW, Huang Y, et al. Orchestrated intron retention regulates normal granulocyte differentiation. Cell. 2013;154:583–95. · DOI · Paper page
  15. Pimentel H, Parra M, Gee S, Ghanem D, An X, Li J, et al. A dynamic alternative splicing program regulates gene expression during terminal erythropoiesis. Nucleic Acids Res. 2014;42:4031–42. · DOI
  16. Fried VA, Smith HT, Hildebrandt E, Weiner K. Ubiquitin has intrinsic proteolytic activity: implications for cellular regulation. Proc Natl Acad Sci U S A. 1987;84:3685–9. · DOI
  17. Thom CS, Traxler EA, Khandros E, Nickas JM, Zhou OY, Lazarus JE, et al. Trim58 degrades Dynein and regulates terminal erythropoiesis. Dev Cell. 2014;30:688–700. · DOI
  18. Song H, Luo J, Luo W, Weng J, Wang Z, Li B, et al. Inactivation of G-protein-coupled receptor 48 (Gpr48/Lgr4) impairs definitive erythropoiesis at midgestation through down-regulation of the ATF4 signaling pathway. J Biol Chem. 2008;283:36687–97. · DOI
  19. Cokic VP, Smith RD, Biancotto A, Noguchi CT, Puri RK, Schechter AN. Globin gene expression in correlation with G protein-related genes during erythroid differentiation. BMC Genomics. 2013;14:116. · DOI
  20. Zhang J, Loyd MR, Randall MS, Waddell MB, Kriwacki RW, Ney PA. A short linear motif in BNIP3L (NIX) mediates mitochondrial clearance in reticulocytes. Autophagy. 2012;8:1325–32. · DOI
  21. Ponka P, Beaumont C, Richardson DR. Function and regulation of transferrin and ferritin. Semin Hematol. 1998;35:35–54.
  22. Conboy J, Kan YW, Shohet SB, Mohandas N. Molecular cloning of protein 4.1, a major structural element of the human erythrocyte membrane skeleton. Proc Natl Acad Sci U S A. 1986;83:9512–6. · DOI · Paper page
  23. Kohno K, Narimatsu H, Shiono Y, Suzuki I, Kato Y, Fukao A, et al. Management of erythropoiesis: cross-sectional study of the relationships between erythropoiesis and nutrition, physical features, and adiponectin in 3519 Japanese people. Eur J Haematol. 2014;92:298–307. · DOI
  24. Friedlander MR, Mackowiak SD, Li N, Chen W, Rajewsky N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 2012;40:37–52. · DOI · Paper page
  25. Swaminathan S, Hu X, Zheng X, Kriga Y, Shetty J, Zhao Y, et al. Interleukin-27 treated human macrophages induce the expression of novel microRNAs which may mediate anti-viral properties. Biochem Biophys Res Commun. 2013;434:228–34. · DOI
  26. Sharbati S, Friedlander MR, Sharbati J, Hoeke L, Chen W, Keller A, et al. Deciphering the porcine intestinal microRNA transcriptome. BMC Genomics. 2010;11:275. · DOI
  27. Jima DD, Zhang J, Jacobs C, Richards KL, Dunphy CH, Choi WW, et al. Deep sequencing of the small RNA transcriptome of normal and malignant human B cells identifies hundreds of novel microRNAs. Blood. 2010;116:e118–127. · DOI
  28. Wang LS, Li L, Li L, Chu S, Shiang KD, Li M, et al. MicroRNA-486 regulates normal erythropoiesis and enhances growth and modulates drug response in CML progenitors. Blood. 2014;125(8):1302–13. · DOI
  29. Shaham L, Vendramini E, Ge Y, Goren Y, Birger Y, Tijssen MR, et al. MicroRNA-486-5p is an erythroid oncomiR of the myeloid leukemias of Down syndrome. Blood. 2014;125(8):1292–301. · DOI · Paper page
  30. Yu D, dos Santos CO, Zhao G, Jiang J, Amigo JD, Khandros E, et al. miR-451 protects against erythroid oxidant stress by repressing 14-3-3zeta. Genes Dev. 2010;24:1620–33. · DOI
  31. Fu YF, Du TT, Dong M, Zhu KY, Jing CB, Zhang Y, et al. Mir-144 selectively regulates embryonic alpha-hemoglobin synthesis during primitive erythropoiesis. Blood. 2009;113:1340–9. · DOI
  32. Patrick DM, Zhang CC, Tao Y, Yao H, Qi X, Schwartz RJ, et al. Defective erythroid differentiation in miR-451 mutant mice mediated by 14-3-3zeta. Genes Dev. 2010;24:1614–9. · DOI
  33. Byon JC, Padilla SM, Papaynnopoulou T. Deletion of Dicer in late erythroid cells results in impaired stress erythropoiesis in mice. Exp Hematol. 2014;42(10):852–6. · DOI
  34. Noh SJ, Miller SH, Lee YT, Goh SH, Marincola FM, Stroncek DF, et al. Let-7 microRNAs are developmentally regulated in circulating human erythroid cells. J Transl Med. 2009;7:98. · DOI
  35. Teruel-Montoya R, Kong X, Abraham S, Ma L, Kunapuli SP, Holinstat M, et al. MicroRNA expression differences in human hematopoietic cell lineages enable regulated transgene expression. PLoS One. 2014;9:e102259. · DOI
  36. Kirschner MB, Kao SC, Edelman JJ, Armstrong NJ, Vallely MP, van Zandwijk N, et al. Haemolysis during sample preparation alters microRNA content of plasma. PLoS One. 2011;6:e24145. · DOI · Paper page
  37. Bruchova H, Yoon D, Agarwal AM, Mendell J, Prchal JT. Regulated expression of microRNAs in normal and polycythemia vera erythropoiesis. Exp Hematol. 2007;35:1657–67. · DOI · Paper page
  38. Chang JT, Nevins JR. GATHER: a systems approach to interpreting genomic signatures. Bioinformatics. 2006;22:2926–33. · DOI
  39. Nakao A, Imamura T, Souchelnytskyi S, Kawabata M, Ishisaki A, Oeda E, et al. TGF-beta receptor-mediated signalling through Smad2, Smad3 and Smad4. EMBO J. 1997;16:5353–62. · DOI
  40. Chen YG, Wang Z, Ma J, Zhang L, Lu Z. Endofin, a FYVE domain protein, interacts with Smad4 and facilitates transforming growth factor-beta signaling. J Biol Chem. 2007;282:9688–95. · DOI · Paper page
  41. Dong XM, Yin RH, Yang Y, Feng ZW, Ning HM, Dong L, et al. GATA-2 inhibits transforming growth factor-beta signaling pathway through interaction with Smad4. Cell Signal. 2014;26:1089–97. · DOI · Paper page
  42. Harada H, Grant S. Apoptosis regulators. Rev Clin Exp Hematol. 2003;7:117–38.
  43. Balsara RD, Ploplis VA. Plasminogen activator inhibitor-1: the double-edged sword in apoptosis. Thromb Haemost. 2008;100:1029–36. · DOI
  44. Ferrandiz N, Caraballo JM, Garcia-Gutierrez L, Devgan V, Rodriguez-Paredes M, Lafita MC, et al. p21 as a transcriptional co-repressor of S-phase and mitotic control genes. PLoS One. 2012;7:e37759. · DOI
  45. Zermati Y, Fichelson S, Valensi F, Freyssinier JM, Rouyer-Fessard P, Cramer E, et al. Transforming growth factor inhibits erythropoiesis by blocking proliferation and accelerating differentiation of erythroid progenitors. Exp Hematol. 2000;28:885–94. · DOI
  46. Randrianarison-Huetz V, Laurent B, Bardet V, Blobe GC, Huetz F, Dumenil D. Gfi-1B controls human erythroid and megakaryocytic differentiation by regulating TGF-beta signaling at the bipotent erythro-megakaryocytic progenitor stage. Blood. 2010;115:2784–95. · DOI · Paper page
  47. Choi SJ, Moon JH, Ahn YW, Ahn JH, Kim DU, Han TH. Tsc-22 enhances TGF-beta signaling by associating with Smad4 and induces erythroid cell differentiation. Mol Cell Biochem. 2005;271:23–8. · DOI · Paper page
  48. Dussiot M, Maciel TT, Fricot A, Chartier C, Negre O, Veiga J, et al. An activin receptor IIA ligand trap corrects ineffective erythropoiesis in beta-thalassemia. Nat Med. 2014;20:398–407. · DOI · Paper page
  49. Suragani RN, Cadena SM, Cawley SM, Sako D, Mitchell D, Li R, et al. Transforming growth factor-beta superfamily ligand trap ACE-536 corrects anemia by promoting late-stage erythropoiesis. Nat Med. 2014;20:408–14. · DOI · Paper page
  50. Dennler S, Itoh S, Vivien D, ten Dijke P, Huet S, Gauthier JM. Direct binding of Smad3 and Smad4 to critical TGF beta-inducible elements in the promoter of human plasminogen activator inhibitor-type 1 gene. EMBO J. 1998;17:3091–100. · DOI
  51. Beelman CA, Parker R. Degradation of mRNA in eukaryotes. Cell. 1995;81:179–83. · DOI
  52. He L, Hannon GJ. MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet. 2004;5:522–31. · DOI · Paper page
  53. Lin S, Cheung WK, Chen S, Lu G, Wang Z, Xie D, et al. Computational identification and characterization of primate-specific microRNAs in human genome. Comput Biol Chem. 2010;34:232–41. · DOI
  54. Lopez JP, Lim R, Cruceanu C, Crapper L, Fasano C, Labonte B, et al. miR-1202 is a primate-specific and brain-enriched microRNA involved in major depression and antidepressant treatment. Nat Med. 2014;20:764–8. · DOI · Paper page
  55. Pishesha N, Thiru P, Shi J, Eng JC, Sankaran VG, Lodish HF. Transcriptional divergence and conservation of human and mouse erythropoiesis. Proc Natl Acad Sci U S A. 2014;111:4103–8. · DOI
  56. Rasmussen KD, Simmini S, Abreu-Goodger C, Bartonicek N, Di Giacomo M, Bilbao-Cortes D, et al. The miR-144/451 locus is required for erythroid homeostasis. J Exp Med. 2010;207:1351–8. · DOI · Paper page
  57. Attie KM, Allison MJ, McClure T, Boyd IE, Wilson DM, Pearsall AE, et al. A phase 1 study of ACE-536, a regulator of erythroid differentiation, in healthy volunteers. Am J Hematol. 2014;89(7):766–70. · DOI
  58. Kersey PJ, Staines DM, Lawson D, Kulesha E, Derwent P, Humphrey JC, et al. Ensembl Genomes: an integrative resource for genome-scale data from non-vertebrate species. Nucleic Acids Res. 2012;40:D91–97. · DOI
  59. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21. · DOI · Paper page
  60. Wang L, Wang S, Li W. RSeQC: quality control of RNA-seq experiments. Bioinformatics. 2012;28:2184–5. · DOI
  61. Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol. 2013;31:46–53. · DOI · Paper page
  62. Witt AE, Hines LM, Collins NL, Hu Y, Gunawardane RN, Moreira D, et al. Functional proteomics approach to investigate the biological activities of cDNAs implicated in breast cancer. J Proteome Res. 2006;5:599–610. · DOI

Cited by

Sorted by citations

Metadata

Authors
Doss, J. F., Corcoran, D. L., Jima, D. D., Telen, M. J., Dave, S. S., & Chi, J.-T.
Venue
BMC Genomics
Publisher
Springer Science and Business Media LLC
Published
2015-11-16
Volume
16
Issue
1
ISSN
1471-2164
DOI
10.1186/s12864-015-2156-2
PMC
PMC4647483
Cited by
113
References
3
Language
en
Funders
  • Doris Duke Charitable Foundation (US) (2011098)
  • World Anti-Doping Agency (CA) (13C31JC)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review