Cited by 148Genomics and Phylogenetic StudiesRNA Research and SplicingSingle-cell and spatial transcriptomics

The DOI matches its Crossref record; 27 of the 28 references it cites were found in a registry or an index.

RNA-sequence analysis of human B-cells

Open access

Toung, Jonathan M., Morley, Michael, Li, Mingyao, Cheung, Vivian G.·2011·Genome Research

Open this paper in CoChat — free account Open PDF

Abstract

RNA-sequencing (RNA-seq) allows quantitative measurement of expression levels of genes and their transcripts. In this study, we sequenced complementary DNA fragments of cultured human B-cells and obtained 879 million 50-bp reads comprising 44 Gb of sequence. The results allowed us to study the gene expression profile of B-cells and to determine experimental parameters for sequencing-based expression studies. We identified 20,766 genes and 67,453 of their alternatively spliced transcripts. More than 90% of the genes with multiple exons are alternatively spliced; for most genes, one isoform is predominantly expressed. We found that while chromosomes differ in gene density, the percentage of transcribed genes in each chromosome is less variable. In addition, genes involved in related biological processes are expressed at more similar levels than genes with different functions. Besides characterizing gene expression, we also used the data to investigate the effect of sequencing depth on gene expression measurements. While 100 million reads are sufficient to detect most expressed genes and transcripts, about 500 million reads are needed to measure accurately their expression levels. We provide examples in which deep sequencing is needed to determine the relative abundance of genes and their isoforms. With data from 20 individuals and about 40 million sequence reads per sample, we uncovered only 21 alternatively spliced, multi-exon genes that are not in databases; this result suggests that at this sequence coverage, we can detect most of the known genes. Results from this project are available on the UCSC Genome Browser to allow readers to study the expression and structure of genes in human B-cells.

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.

Integrity signals

Registry

Crossref · 10.1101/gr.116335.110

Cites

27 of 27 references verified0 citation errors · 0 not checkable

Statistics

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.

Self-check

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

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

Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
TopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,682
Alternative isoform regulation in human tissue transcriptomes
Wang, E. T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S. F., Schroth, G. P., & Burge, C. B. (2008) · Nature · doi:10.1038/nature07509 · Cited by 5,181
Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing
Pan, Q., Shai, O., Lee, L. J., Frey, B. J., & Blencowe, B. J. (2008) · Nature Genetics · doi:10.1038/ng.259 · Cited by 3,951
Serial Analysis of Gene Expression
Velculescu, V. E., Zhang, L., Vogelstein, B., & Kinzler, K. W. (1995) · Science · doi:10.1126/science.270.5235.484 · Cited by 3,713
Accurate whole human genome sequencing using reversible terminator chemistry
Bentley, D. R., Balasubramanian, S., Swerdlow, H. P., Smith, G. P., Milton, J., Brown, C. G., Hall, K. P., Evers, D. J., Barnes, C. L., Bignell, H. R., et al. (2008) · Nature · doi:10.1038/nature07517 · Cited by 3,684
The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing
Nagalakshmi, U., Wang, Z., Waern, K., Shou, C., Raha, D., Gerstein, M., & Snyder, M. (2008) · Science · doi:10.1126/science.1158441 · Cited by 2,479

Flagged first, then by citations

Show all 28 references

1 of the 28 references couldn’t be matched to a known paper.

Sorted by citations

Items 101–148 of 148

Transcriptomic analysis of rice aleurone cells identified a novel abscisic acid response element
Watanabe, K. A., Homayouni, A. L., Gu, L., Huang, K.-Y. A., Ho, T. D., & Shen, Q. J. (2017) · Plant Cell & Environment · doi:10.1111/pce.13006 · Cited by 10
Comparison of the genomes and transcriptomes associated with the different protease secretions of Aspergillus oryzae 100-8 and 3.042
Zhao, G., Yao, Y., Hou, L., Wang, C., & Cao, X. (2014) · Biotechnology Letters · doi:10.1007/s10529-014-1574-6 · Cited by 9
Unique Transcriptome Changes in Peripheral B Cells Revealed by Comparing Age Groups From Naive or Vaccinated Mice, Including snoRNA and Cdkn2a
Baudier, R. L., Zwezdaryk, K. J., Czarny‐Ratajczak, M., Kodroff, L. H., Sullivan, D. E., & Norton, E. B. (2020) · The Journals of Gerontology Series A · doi:10.1093/gerona/glaa165 · Cited by 8
Transcriptome landscape of human primary monocytes at different sequencing depth
Mirsafian, H., Ripen, A. M., Leong, W.-M., Manaharan, T., Mohamad, S. B., & Merican, A. F. (2017) · Genomics · doi:10.1016/j.ygeno.2017.07.003 · Cited by 8
Mycobacterium abscessus persistence in the face of Pseudomonas aeruginosa antagonism
Gupta, R., Schuster, M., & Rohde, K. H. (2025) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2025.1569331 · Cited by 6
Methods for the analysis of transcriptome dynamics
Rodrigues, D. F., Costa, V. M., Silvestre, R., Bastos, M. D. L., & Carvalho, F. D. (2019) · Toxicology Research · doi:10.1039/c9tx00088g · Cited by 6
Identification and Validation of Gene Expression Patterns in Cystitis Glandularis Patients and Controls
Chu, C., Zhao, C., Zhang, Z., Wang, M., Zhang, Z., Yang, A., Ma, B., Lu, C.-F., Wu, M., Gu, M., et al. (2017) · SLAS DISCOVERY · doi:10.1177/2472555216685519 · Cited by 6
Human Lymphoblastoid Cell Lines in Pharmacogenomics
Jeon, J. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-386882-4.00006-2 · Cited by 5
Pattern recognition receptor ligand-induced differentiation of human transitional B cells
McMillan, J. K. P., O'Donnell, P. J., & Chang, S. P. (2022) · PLoS ONE · doi:10.1371/journal.pone.0273810 · Cited by 5
Comparing control options for time‐series RNA sequencing experiments in nonmodel organisms: An example from grasses
Qiu, F., Bachle, S., Nippert, J. B., & Ungerer, M. C. (2020) · Molecular Ecology Resources · doi:10.1111/1755-0998.13137 · Cited by 4
Translatomics to explore dynamic differences in immunocytes in the tumor microenvironment
Guo, Y., Yan, S., & Zhang, W. (2023) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2023.102037 · Cited by 4
Leptin inhibits AMPKα2 down-regulation induced decrease in the osteocytic MLO-Y4 cell proliferation and the expression of osteogenic markers.
Fan, Q., Li, H., Liu, Z., Zhang, Z., Li, H., Ding, J., & Zhang, Z. (2017) · PubMed Central · Cited by 4
The research progress of human genetic diversity in China
Yang, Z., & Chu, J. (2012) · Hereditas (Beijing) · doi:10.3724/sp.j.1005.2012.01351 · Cited by 3
Analysis of the transcription factors expressed in the mature seed embryos of Moringa oleifera Lam. using RNA-sequencing and de novo transcriptome assembly
Panes, V. A., & Baoas, R. D. (2018) · Acta Horticulturae · doi:10.17660/actahortic.2018.1205.87 · Cited by 3
Simultaneous SNP identification and assessment of allele-specific bias from ChIP-seq data
Ni, Y., Hall, A. W., Battenhouse, A. M., & Iyer, V. R. (2012) · BMC Genetics · doi:10.1186/1471-2156-13-79 · Cited by 3
Genome sequencing of Aspergillus glaucus ‘CCHA’ provides insights into salt-stress adaptation
Qiu, W., Li, J., Wei, Y., Fan, F., Jiang, J., Liu, M., Han, X., Tian, C., Zhang, S., & Zhuo, R. (2020) · PeerJ · doi:10.7717/peerj.8609 · Cited by 3
Beta-hydroxybutyrate infusion identifies acutely differentially expressed genes related to metabolism and reproduction in the hypothalamus and pituitary of castrated male sheep
Cope, E. R., Voy, B. H., Whitlock, B. K., Staton, M. E., Lane, T. S., Davitt, J., & Mulliniks, J. T. (2018) · Physiological Genomics · doi:10.1152/physiolgenomics.00104.2017 · Cited by 2
Endothelial transcriptomic, epigenomic and proteomic data challenge the proposed role for TSAd in vascular permeability
Brash, J. T., Diez-Pinel, G., Rinaldi, L., Castellan, R. F. P., Fantin, A., & Ruhrberg, C. (2025) · Angiogenesis · doi:10.1007/s10456-025-09971-x · Cited by 2
Suitability of Illumina deep mRNA sequencing for reliable gene expression profiling in a non-model conifer species (Pseudotsuga menziesii)
Hess, M., Wildhagen, H., & Ensminger, I. (2013) · Tree Genetics & Genomes · doi:10.1007/s11295-013-0656-2 · Cited by 2
In Vivo Interrogation of the Hypoxic Transcriptome of Solid Tumors: Optimizing Hypoxic Probe Labeling with Laser Capture Microdissection for Isolation of High-Quality RNA for Deep Sequencing Analysis
Brady, L., Popov, V. M., & Koumenis, C. (2016) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-26666-4_4 · Cited by 2
Long Non-coding RNA Expression Profiling Using Arraystar LncRNA Microarrays
Shi, Y., & Shang, J. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1697-0_7 · Cited by 2
Sex-specific expression during embryonic development of Anopheles gambiae
Dennison, N. J. (2012) · University of Liverpool · doi:10.17638/00006373 · Cited by 2
Post-transcriptional regulation by TIA1 and TIAL1 controls the transcriptional program enforcing T cell quiescencePreprint
Osma-García, I. C., Maloudi, O., Mouysset, M., Capitan-Sobrino, D., Nguyen, T.-M. M., Aubert, Y., & Díaz‐Muñoz, M. D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.03.608755 · Cited by 2
RNA-Seq: Yellow Nutsedge (Cyperus esculentus) transcriptome analysis of lipid-accumulating tubers from early to late developmental stages
Thieffry, A. (2014) · Epsilon Archive for Student Projects (University of Southampton) · Cited by 2
A-to-I Editing Is Subtype-Specific in Non-Hodgkin Lymphomas
Chen, C., & Bundschuh, R. A. (2024) · Genes · doi:10.3390/genes15070864 · Cited by 1
Bioinformatics Analysis of Pancreas Cancer Genome in High-Throughput Genomic Technologies
Pau, E. C. D. S., Real, F. X., & Valencia, A. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-408103-1.00005-4 · Cited by 2
scRNA-seq of preeclamptic trophoblasts identifies EBI3, COL17A1, miR-27a-5p, and miR-193b-5p as hypoxia markers: validation of neuradapt as a superior mimetic to cobalt chloride
Knyazev, E. N., Kulagin, T., Antipenko, I. D., & Tonevitsky, A. (2026) · Placenta · doi:10.1016/j.placenta.2026.02.005 · Cited by 1
Studies of Body Systems
Merican, A. F., Mirsafian, H., Ripen, A. M., & Mohamad, S. B. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.20437-8 · Cited by 1
RNA-Editing-Initiated MAVS Signaling is a Key Epitranscriptomic Alteration in Human B Cell LymphomaPreprint
Pecori, R., Ren, W., Wang, X., Berglund, M., Li, W., Tasakis, R. N., Giorgio, S. D., Ye, X., Arnold, A., Wüst, S., et al. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3927430 · Cited by 1
RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION
Cai, G. (2013) · Digital Commons-TMC (Texas Medical Center)
Quo vadis cellula
Kessi, C. E. (2012) · Avances en Ciencias Veterinarias · doi:10.5354/acv.v27i1.21996
Algorithms and applications of next-generation DNA sequencing : ChIP-Seq, database of human variations, and analysis of mammary ductal carcinomas
Fejes, A. P. (2012) · cIRcle (University of British Columbia) · doi:10.14288/1.0072704
Characterization of the Iberian pig genome and transcriptome using high throughput sequencing
Esteve‐Codina, A. (2012) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)
Quantitative Models Identify Histone Signatures of Poised Genes Prior to Cellular DifferentiationPreprint
Tian, R. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/137646
Bioinformatics to integrate protein and gene information in a relational context, application to human proteomic and transcriptomic data
Droste, C. F. (2017) · doi:10.14201/gredos.135785
E3 Ligase UBR5 HECT domain mutations in lymphoma control maturation of B cells via alternative splicingPreprint
Swenson, S. A., Gilbreath, T. J., Vahle, H., Hynes-Smith, R. W., Graham, J. H., Law, H. C.-H., Woods, N. T., Green, M. R., & Buckley, S. M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/732180
Role of Chemokine and TNF signaling pathway in oral squamous cell carcinoma: A RNA deep sequencing analysis of oral buccal mucosa squamous carcinoma model of Chinese hamsterPreprint
Xu, G., Wei, J., Huangfu, B., Gao, J., Wang, X., Xiao, L., Xuan, R., Chen, Z., & Song, G. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/778399
Comparison of the genomes and transcriptomes associated with the different protease secretions of Aspergillus oryzae 100-8 and 3.042
Hou, G. Z. Y., & Cao, C. W. (2014)
Discovering genes involved in the synthesis of secondary metabolites from the seeds of Moringa oleifera through transcriptome analysis
Panes, V. A., & Discar, M. D. K. (2021) · Acta Horticulturae · doi:10.17660/actahortic.2021.1306.7
Additional file 6 of Genome-wide identification of long noncoding RNAs and their competing endogenous RNA networks involved in the odontogenic differentiation of human dental pulp stem cells
Zhao, C., Zhang, K., Qiu, W., Luo, Y., Pan, Y., Li, J., Yang, Y., Wu, B., & Fang, F. (2020) · Figshare · doi:10.6084/m9.figshare.11984763
Additional file 2 of Genome-wide identification of long noncoding RNAs and their competing endogenous RNA networks involved in the odontogenic differentiation of human dental pulp stem cells
Zhao, C., Zhang, K., Qiu, W., Luo, Y., Pan, Y., Li, J., Yang, Y., Wu, B., & Fang, F. (2020) · Figshare · doi:10.6084/m9.figshare.11984730
Additional file 3 of Genome-wide identification of long noncoding RNAs and their competing endogenous RNA networks involved in the odontogenic differentiation of human dental pulp stem cells
Zhao, C., Zhang, K., Qiu, W., Luo, Y., Pan, Y., Li, J., Yang, Y., Wu, B., & Fang, F. (2020) · Figshare · doi:10.6084/m9.figshare.11984739
Additional file 4 of Genome-wide identification of long noncoding RNAs and their competing endogenous RNA networks involved in the odontogenic differentiation of human dental pulp stem cells
Zhao, C., Zhang, K., Qiu, W., Luo, Y., Pan, Y., Li, J., Yang, Y., Wu, B., & Fang, F. (2020) · Figshare · doi:10.6084/m9.figshare.11984748
Additional file 5 of Genome-wide identification of long noncoding RNAs and their competing endogenous RNA networks involved in the odontogenic differentiation of human dental pulp stem cells
Zhao, C., Zhang, K., Qiu, W., Luo, Y., Pan, Y., Li, J., Yang, Y., Wu, B., & Fang, F. (2020) · Figshare · doi:10.6084/m9.figshare.11984754
Additional file 1 of Genome-wide identification of long noncoding RNAs and their competing endogenous RNA networks involved in the odontogenic differentiation of human dental pulp stem cells
Zhao, C., Zhang, K., Qiu, W., Luo, Y., Pan, Y., Li, J., Yang, Y., Wu, B., & Fang, F. (2020) · Figshare · doi:10.6084/m9.figshare.11984724
Studies of Body Systems
Merican, A. F., Mirsafian, H., Ripen, A. M., & Mohamad, S. B. (2019) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00352-3
The emerging roles of alternative splicing in modulating tumor immune responses and immunotherapies
He, X., Zhao, D., Zhou, Y., Jiang, Y., Dong, Z., & Liu, K. (2026) · Cell Death and Differentiation · doi:10.1038/s41418-026-01792-9
Décrypter les données omiques : importance du contrôle qualité. Application au cancer de l'ovaire
Sambourg, L. (2013) · doi:10.70675/1d91eb8bzbee9z49b6zbccfzeffd6c25336e

Authors: Toung, J. M., Morley, M., Li, M., & Cheung, V. G.
Affiliations: University of Pennsylvania (US)
Children's Hospital of Philadelphia (US)
ORCID: Morley, Michael
Li, Mingyao
Cheung, Vivian G.
Venue: Genome Research
Publisher: Cold Spring Harbor Laboratory
Published: 2011-05-02
Volume: 21
Issue: 6
Pages: 991-998
ISSN: 1088-9051 (ISSN-L)
DOI: 10.1101/gr.116335.110
PMC: PMC3106332
OpenAlex: W2142108845
Cited by: 142
References: 28
Citations by year: 2026: 2 · 2025: 3 · 2024: 3 · 2023: 2 · 2022: 2 · 2021: 7 · 2020: 8 · 2019: 4 · 2018: 9 · 2017: 14 · 2016: 14 · 2015: 8 · 2014: 20 · 2013: 19 · 2012: 20
Type: posted content
Language: en
Open access: bronze
doi.org
Locations: Genome Research (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
HighWire Press Open Archive (submittedVersion)
Funders: Howard Hughes Medical Institute
National Institutes of Health
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Keywords: human B cells, gene expression profiling, alternative splicing, transcriptome, gene isoforms, sequencing depth, gene density, gene discovery, UCSC Genome Browser
MeSH terms: B-Lymphocytes, Humans, Proteins, Sequence Analysis, RNA, DNA, Complementary, Protein Isoforms, Gene Expression Profiling, High-Throughput Nucleotide Sequencing

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

RNA-sequence analysis of human B-cells

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks