Sequencing technology does not eliminate biological variability

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

RNA-Seq: a revolutionary tool for transcriptomics

Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring

Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., et al. (1999) · Science · doi:10.1126/science.286.5439.531 · Cited by 11,516

Stochastic Gene Expression in a Single Cell

Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. (2002) · Science · doi:10.1126/science.1070919 · Cited by 5,441

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

RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays

Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., & Gilad, Y. (2008) · Genome Research · doi:10.1101/gr.079558.108 · Cited by 2,771

Tackling the widespread and critical impact of batch effects in high-throughput data

Leek, J. T., Scharpf, R. B., Bravo, H. C., Simcha, D., Langmead, B., Johnson, W. E., Geman, D., Baggerly, K., & Irizarry, R. A. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2825 · Cited by 2,196

The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements

MAQC Consortium, Shi, L., Shi, L., Reid, L. H., Jones, W. D., Shippy, R., Warrington, J. A., Baker, S. C., Collins, P. J., de Longueville, F., et al. (2006) · Nature Biotechnology · doi:10.1038/nbt1239 · Cited by 2,126

Relative Impact of Nucleotide and Copy Number Variation on Gene Expression Phenotypes

Stranger, B. E., Forrest, M. S., Dunning, M., Ingle, C., Beazley, C., Thorne, N., Redon, R., Bird, C., Grassi, A., Lee, C., et al. (2007) · Science · doi:10.1126/science.1136678 · Cited by 1,743

Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments

Bullard, J. H., Purdom, E., Hansen, K. D., & Dudoit, S. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-94 · Cited by 1,680

Effect of high variation in transcript expression on identifying differentially expressed genes in RNA‐seq analysis

Cui, W., Xue, H., Geng, Y., Zhang, J., Liang, Y., Tian, X., & Wang, Q. (2021) · Annals of Human Genetics · doi:10.1111/ahg.12441 · Cited by 16

PerC Manipulates Metabolism and Surface Antigens in Enteropathogenic Escherichia coli

Mellies, J. L., Platenkamp, A., Osborn, J. F., & Ben‐Avi, L. (2017) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2017.00032 · Cited by 14

Detecting antibody reactivities in Phage ImmunoPrecipitation Sequencing data

Chen, A., Kammers, K., Larman, H. B., Scharpf, R. B., & Ruczinski, I. (2022) · BMC Genomics · doi:10.1186/s12864-022-08869-y · Cited by 14

Next generation quantitative genetics in plants

Jiménez‐Gómez, J. M. (2011) · Frontiers in Plant Science · doi:10.3389/fpls.2011.00077 · Cited by 16

A Leveraged Signal-to-Noise Ratio (LSTNR) Method to Extract Differentially Expressed Genes and Multivariate Patterns of Expression From Noisy and Low-Replication RNAseq Data

Lozoya, O. A., Santos, J. H., & Woychik, R. P. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00176 · Cited by 14

Deep sequencing of small RNAs in plants: applied bioinformatics

Studholme, D. J. (2011) · Briefings in Functional Genomics · doi:10.1093/bfgp/elr039 · Cited by 13

Using the Constrained Disorder Principle to Navigate Uncertainties in Biology and Medicine: Refining Fuzzy Algorithms

Ilan, Y. (2024) · Biology · doi:10.3390/biology13100830 · Cited by 16

Transcriptome Analysis for Abnormal Spike Development of the Wheat Mutant dms

Zhu, X.-X., Li, Q.-Y., Shen, C.-C., Duan, Z.-B., Dongyan, Y., Niu, J., Ni, Y.-J., & Jiang, Y.-M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0149287 · Cited by 12

Synthesizing genome regulation data with vote-counting

Fischer, M., & Hoffmann, S. (2022) · Trends in Genetics · doi:10.1016/j.tig.2022.06.012 · Cited by 12

Strategy for RNA-Seq Experimental Design and Data Analysis

Gimenez, G., Stockwell, P. A., Rodger, E. J., & Chatterjee, A. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2780-8_16 · Cited by 12

Statistical detection of differentially abundant ions in mass spectrometry-based imaging experiments with complex designs

Bemis, K. A., Guo, D., Harry, A. J., Thomas, M., Lanekoff, I., Stenzel‐Poore, M. P., Stevens, S. L., Laskin, J., & Vitek, O. (2018) · International Journal of Mass Spectrometry · doi:10.1016/j.ijms.2018.07.006 · Cited by 12

Functional Genomics, Proteomics, Metabolomics and Bioinformatics for Systems Biology

Ballereau, S. J., Glaab, E., Kolodkin, A. N., Chaiboonchoe, A., Biryukov, M., Vlassis, N. A., Ahmed, H., Pellet, J., Baliga, N. S., Hood, L., et al. (2013) · Springer eBooks · doi:10.1007/978-94-007-6803-1_1 · Cited by 10

Minireview: Applications of Next-Generation Sequencing on Studies of Nuclear Receptor Regulation and Function

Meyer, C. A., Tang, Q., & Liu, X. S. (2012) · Molecular Endocrinology · doi:10.1210/me.2012-1150 · Cited by 11

Transcriptomic Responses of Human Retinal Vascular Endothelial Cells to Inflammatory Cytokines

Ryan, F. J., Ma, Y., Ashander, L. M., Kvopka, M., Appukuttan, B., Lynn, D. J., & Smith, J. R. (2022) · Translational Vision Science & Technology · doi:10.1167/tvst.11.8.27 · Cited by 11

Transcriptome reveals molecular mechanism of cabbage response to low temperature stress and functional study of BoPYL8 gene

Li, Q., Cai, Y., Gu, L., Yu, X., Wang, Y., Zhang, G., Zhao, Y., Abdullah, S., & Li, P. (2023) · Scientia Horticulturae · doi:10.1016/j.scienta.2023.112523 · Cited by 11

Estimating differential expression from multiple indicators

Ilmjärv, S., Hundahl, C. A., Reimets, R., Niitsoo, M., Kolde, R., Vilo, J., Vasar, E., & Luuk, H. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku158 · Cited by 11

Detection and characterization of microRNA expression profiling and its target genes in response to canine parvovirus in Crandell Reese Feline Kidney cells

Chuammitri, P., Vannamahaxay, S., Sornpet, B., Pringproa, K., & Patchanee, P. (2020) · PeerJ · doi:10.7717/peerj.8522 · Cited by 11

Cytological Observation and Transcriptome Comparative Analysis of Self-Pollination and Cross-Pollination in Dendrobium Officinale

Chen, Y., Hu, B., Zhang, F., Luo, X., & Xie, J. (2021) · Genes · doi:10.3390/genes12030432 · Cited by 10

AuPairWise: A Method to Estimate RNA-Seq Replicability through Co-expression

Ballouz, S., & Gillis, J. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004868 · Cited by 12

Design, challenges, and the potential of transcriptomics to understand social behavior

Lim, W. K., & Mathuru, A. S. (2020) · Current Zoology · doi:10.1093/cz/zoaa007 · Cited by 11

Identification of pathological transcription in autosomal dominant polycystic kidney disease epithelia

Friedrich, S., Müller, H., Riesterer, C., Schüller, H., Friedrich, K., Wörner, C. L., Busch, T., Viau, A., Kuehn, E. W., Köttgen, M., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-94442-8 · Cited by 9

Dynamic changes of phytohormone signaling in the base of Taxus media stem cuttings during adventitious root formation

Ren, H., Hu, H., Luo, X., Zhang, C., Li, X., Li, P., Li, W., Ali, K., Sun, X., Ren, Z., et al. (2018) · Scientia Horticulturae · doi:10.1016/j.scienta.2018.11.014 · Cited by 8

Transcriptomic profiling and discovery of key transcription factors involved in adventitious roots formation from root cuttings of mulberry

Dou, H., Sun, J., Wang, T., Bi, S., Feng, X., Sun, H., & Quan, J. (2024) · BMC Genomics · doi:10.1186/s12864-024-10593-8 · Cited by 7

MyBrain-Seq: A Pipeline for MiRNA-Seq Data Analysis in Neuropsychiatric Disorders

Pérez-Rodríguez, D., Agís‐Balboa, R. C., & López-Fernández, H. (2023) · Biomedicines · doi:10.3390/biomedicines11041230 · Cited by 7

MSIQ: Joint modeling of multiple RNA-seq samples for accurate isoform quantification

Li, W. V., Zhao, A., Zhang, S., & Li, J. J. (2018) · The Annals of Applied Statistics · doi:10.1214/17-aoas1100 · Cited by 5

Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data

Wilfinger, W. W., Miller, R. G., Eghbalnia, H. R., Mackey, K., & Chomczyński, P. (2021) · BMC Genomics · doi:10.1186/s12864-021-07563-9 · Cited by 6

Study on the differences of gene expression between pear and apple wild cultivation materials based on RNA-seq technique

Zhang, H., Li, M., Kong, M., Dunwell, J. M., Zhang, Y., Yue, C., Wu, J., & Zhang, S. (2021) · BMC Plant Biology · doi:10.1186/s12870-021-03051-0 · Cited by 6

Understanding Complex Transcriptome Dynamics in Schizophrenia and Other Neurological Diseases Using RNA Sequencing

Wang, X., & Cairns, M. J. (2014) · International review of neurobiology · doi:10.1016/b978-0-12-801105-8.00006-0 · Cited by 6

From sequencing to validation: NGS-based exploration of plasma miRNA in papillary thyroid carcinoma

Cui, W., Xuan, T., Liao, T., & Wang, Y. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1410110 · Cited by 5

Gene expression profiling of corpus luteum reveals the importance of immune system during early pregnancy in domestic sheepPreprint

Pokharel, K., Peippo, J., Weldenegodguad, M., Honkatukia, M., Li, M.-H., & Kantanen, J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/673558 · Cited by 5

Robust identification of perturbed cell types in single-cell RNA-seq data

Nicol, P. B., Paulson, D., Qian, G., Liu, X. S., Irizarry, R., & Sahu, A. D. (2023) · doi:10.1101/2023.05.06.539326 · Cited by 33

mNSF: multi-sample non-negative spatial factorization

Wang, Y., Woyshner, K., Sriworarat, C., Stein-O’Brien, G., Goff, L. A., & Hansen, K. D. (2025) · Genome biology · doi:10.1186/s13059-025-03601-x · Cited by 5

Guidelines for Setting Up a mRNA Sequencing Experiment and Best Practices for Bioinformatic Data Analysis

Galise, T. R., Esposito, S., & D’Agostino, N. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-1201-9_10 · Cited by 4

Transcriptome analysis of strawberry (Fragaria × ananasa) responsive to Colletotrichum gloeosporioides inoculation and mining of resistance genes

Liang, W., Wang, M., Du, B., Ling, L., Bi, Y., Zhang, J., Sun, Y., Zhou, S. Y., Zhang, L., Ma, X., et al. (2022) · Biotechnology & Biotechnological Equipment · doi:10.1080/13102818.2022.2106886 · Cited by 4

Full-length transcript alterations in human bronchial epithelial cells with U2AF1 S34F mutations

Soulette, C. M., Hrabeta‐Robinson, E., Arevalo, C., Felton, C., Tang, A. D., Marin, M. G., & Brooks, A. N. (2023) · Life Science Alliance · doi:10.26508/lsa.202000641 · Cited by 4

CCLHunter: An efficient toolkit for cancer cell line authentication

Bu, C., Zheng, X., Mai, J., Nie, Z., Zeng, J., Qian, Q., Xu, T., Sun, Y., Bào, Y., & Xiao, J. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.09.040 · Cited by 4

A progestin regulates the prostaglandin pathway in the neuroendocrine system in female mudskipper Boleophthalmus pectinirostris

Ma, H., Hong, W. ‐., & Chen, S. X. (2023) · The Journal of Steroid Biochemistry and Molecular Biology · doi:10.1016/j.jsbmb.2023.106300 · Cited by 3

Identification and validation of differentially expressed genes in intramuscular fat metabolism in Guizhou yellow chickens using RNA-Seq analysis

Tian, Y., Wang, X., Rao, Y., Zhou, X., Jiang, Y., Liu, Q., Wu, S. N., & Zhang, F. (2025) · PLoS ONE · doi:10.1371/journal.pone.0326128 · Cited by 3

Wood transcriptome analysis and expression variation of lignin biosynthetic pathway transcripts in Ailanthus excelsa Roxb., a multi-purpose tropical tree species

Dasgupta, M. G., Parveen, A. B. M., Rajasugunasekar, D., & Ulaganathan, K. (2021) · Journal of Biosciences · doi:10.1007/s12038-021-00218-7 · Cited by 3

On taming the effect of transcript level intra-condition count variation during differential expression analysis: A story of dogs, foxes and wolves

Lobo, D., Linheiro, R. S., Godinho, R., & Archer, J. P. (2022) · PLoS ONE · doi:10.1371/journal.pone.0274591 · Cited by 2

Patterns, Profiles, and Parsimony: Dissecting Transcriptional Signatures From Minimal Single-Cell RNA-Seq Output With SALSA

Lozoya, O. A., McClelland, K. S., Papas, B. N., Li, J., & Yao, H. H. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.511286 · Cited by 2

Development of an End-to-End Total RNA Sequencing Quality Control Framework for Blood-Based Biomarker Discovery

Sesler, C. L., Shaginurova, G. I., Wylezinski, L. S., Grigorenko, E. V., Cockerill, F. R., & Spurlock, C. F. (2025) · Journal of Molecular Diagnostics · doi:10.1016/j.jmoldx.2025.05.008 · Cited by 2

Identification and correction of time-series transcriptomic anomalies

Campione, S. A., Kelliher, C., Roth, C., Cho, C.-Y., Deckard, A., Motta, F. C., & Haase, S. B. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf524 · Cited by 2

Transcriptomic and physiological responses of Epinephelus akaara to chronic cold stress

Li, J. S., Shi, H., Ran, S., Zhang, J., Yin, X., Ping, H., Zhang, T., Li, J., Li, B., & Ye, Y. (2025) · Aquaculture · doi:10.1016/j.aquaculture.2025.743584 · Cited by 2

On the Reproducibility of MiRNA-Seq Differential Expression Analyses in Neuropsychiatric Diseases

Pérez-Rodríguez, D., López-Fernández, H., & Agís‐Balboa, R. C. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-86258-9_5 · Cited by 2

DE-FPCA: Testing Gene Differential Expression and Exon Usage Through Functional Principal Component Analysis

Xiong, H., Brown, J. B., Boley, N. P., Bickel, P. J., & Huang, H. (2014) · Springer eBooks · doi:10.1007/978-3-319-07212-8_7 · Cited by 2

Charting Shifts in Saccharomyces cerevisiae Gene Expression across Asynchronous Time Trajectories with Diffusion Maps

Reiter, T. E., Montpetit, R., Runnebaum, R. C., Brown, C. T., & Montpetit, B. (2021) · mBio · doi:10.1128/mbio.02345-21 · Cited by 2

Labour classified by cervical dilatation & fetal membrane rupture demonstrates differential impact on RNA-seq data for human myometrium tissues

Lai, P. F., Lei, K., Zhan, X., Sooranna, G., Li, J. K. H., Georgiou, E. X., Das, A., Singh, N., Li, Q., Stanfield, Z., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0260119 · Cited by 3

Large-Scale Meta-Longitudinal Microbiome Data with a Known Batch Factor

Oh, S., & Li, R. W. (2022) · Genes · doi:10.3390/genes13030392 · Cited by 2

Bayesian Modeling Approaches for Temporal Dynamics in RNA-seq Data

Oh, S., & Song, S. (2018) · InTech eBooks · doi:10.5772/intechopen.73062 · Cited by 2

Not by systems alone: replicability assessment of disease expression signalsPreprint

Ballouz, S., Dörfel, M. J., Crow, M., Crain, J., Faivre, L. O., Keegan, C. E., Kitsiou‐Tzeli, S., Tzetis, M., Lyon, G. J., & Gillis, J. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/128439 · Cited by 1

Transcriptomics provides a genetic signature of vineyard site with insight into vintage-independent regional wine characteristicsPreprint

Reiter, T. E., Montpetit, R., Byer, S., Frias, I., Leon, E., Viano, R., McLoughlin, M. J., Halligan, T., Hernandez, D., Figueroa-Balderas, R. E., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.07.425830 · Cited by 2

On the illusion of scRNA-seq batch effect correctionPreprint

Codicè, F., Fariselli, P., & Raimondi, D. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.12.653389 · Cited by 2

Bioinformatics and Next- generation Sequencing

Krejčí, A., Müller, P., & Vojtěšek, B. (2015) · Klinicka onkologie · doi:10.14735/amko20152s91 · Cited by 1

The medicalization of inactivity

Joyner, M. J., & Barnes, J. N. (2013) · Physiology News · doi:10.36866/pn.90.18 · Cited by 1

Influence of RNA‐Seq library construction, sampling methods, and tissue harvesting time on gene expression estimation

Chiari, Y., Howard, L., Moreno, N., Relyea, S., Dunnigan, J. L., Boyer, M. C., Kardos, M. D., Glaberman, S. R., & Luikart, G. (2023) · Molecular Ecology Resources · doi:10.1111/1755-0998.13757 · Cited by 1

Designing spatial transcriptomic experiments

Righelli, D., Sottosanti, A., & Risso, D. (2023) · Nature Methods · doi:10.1038/s41592-023-01801-6 · Cited by 1

Microbial strategy shifts driven by bamboo invasion impair soil C-N-P cycling in subtropical forests

Li, J., Wang, H., Chen, Q., An, Q.-M., Zhang, T., & Di, D. (2025) · Plant and Soil · doi:10.1007/s11104-025-07920-7 · Cited by 1

Differential Gene Expression in Healing and Non‐Healing Diabetic Foot Ulcers and Discovery of Novel Ratiometric Biomarker to Predict Healing Outcome

Eager, J. M., Cortés-Troncoso, J., Kalan, L., Campbell, A. E., Rao, A. S., Weingarten, M. S., Grice, E. A., Oropallo, A., Margolis, D. J., Saçan, A., et al. (2025) · Wound Repair and Regeneration · doi:10.1111/wrr.70113 · Cited by 1

Quantifying Entire Transcriptomes by Aligned RNA-Seq Data

Calogero, R., & Zolezzi, F. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-2291-8_10 · Cited by 1

New Insights into Bayesian Inference

Nezhad, M. S. F. (2018) · InTech eBooks · doi:10.5772/intechopen.70722 · Cited by 1

RiboSimR: A Tool for Simulation and Power Analysis of Ribo-seq Data

Perkins, P., Stepanova, A. N., Alonso, J. M., & Heber, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46165-2_10 · Cited by 1

Sensitive and accurate analysis of gene expression signatures enabled by oligonucleotide-labelled cDNA

Kapustina, Ž., Medžiūnė, J., Dubovskaja, V., Matjošaitis, K., Žeimytė, S., & Lubys, A. (2022) · RNA Biology · doi:10.1080/15476286.2022.2078093 · Cited by 1

Plasmodium falciparum schizont stage transcriptome variation among clinical isolates and laboratory-adapted clonesPreprint

Tarr, S. J., Díaz-Ingelmo, O., Stewart, L. B., Hocking, S. E., Murray, L., Duffy, C. W., Otto, T. D., Chappell, L., Rayner, J. C., Awandare, G. A., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/329532 · Cited by 1

A Re-Analysis of an Existing Drosophila melanogaster Dataset Reveals a New Set of Genes Involved in Post-Mating ResponsePreprint

Bennett, C. J., & Aramayo, R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.10.588867 · Cited by 1

Transcriptome Analysis

Velayutham, D., Balyan, M. K., Nisamudheen, N. A., Balyan, P., & Jithesh, P. V. (2025) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00292-x · Cited by 1

Measurement, Summary, and Methodological Variation in RNA-sequencing

Frazee, A. C., Torres, L. C., Jaffe, A. E., Langmead, B., & Leek, J. T. (2014) · Springer eBooks · doi:10.1007/978-3-319-07212-8_6

Progression of RNA-sequencing to single-cell applications

Ramsköld, D. (2014) · Figshare

Dynamic Model for RNA-seq Data Analysis

Li, L., & Xiong, M. (2015) · BioMed Research International · doi:10.1155/2015/916352

Genetic Studies of Physiological Traits with Their Application to Sleep ApneaPreprint

Lee, D. Y., Hanis, C. L., Bell, G. I., Aguilar, D. A., Redline, S. S., Below, J. E., & Xiong, M. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.7363 · Cited by 1

Statistical methods for deep sequencing data

Shen, S., Chaloner, K., Davidson, B. L., Murray, J. C., Xing, Y., & Huang, J. (2012) · doi:10.17077/etd.73qfll6a

METHOD Open Access

Maretty, L., Sibbesen, J. A., & Krogh, A. (2014)

The bsseq user’s guide

Hansen, K. D. (2013)

variation of alternative splicing using RNA-seq data

Zhao, K., Lu, Z., Park, J. W., Zhou, Q., & Xing, Y. (2013)

Identification of the molecular changes underlying head morphology variation in closely related Drosophila species

Torres-Oliva, M. (2016) · doi:10.53846/goediss-5851

Establishing Informative Prior for Gene Expression Variance from Public Databases

Li, N., McCall, M. N., & Wu, Z. J. (2016) · Statistics in Biosciences · doi:10.1007/s12561-016-9172-x

THE NUANCES OF STATISTICALLY ANALYZING NEXT-GENERATION SEQUENCING DATA

Srivastava, S., & Doerge, R. W. (2012) · Conference on Applied Statistics in Agriculture · doi:10.4148/2475-7772.1038

Statistical Methods for Two Problems in Cancer Research: Analysis of RNA-seq Data from Archival Samples and Characterization of Onset of Multiple Primary Cancers

Li, J. (2017) · Digital Commons-TMC (Texas Medical Center)

New Statistical Methods of Single-subject Transcriptome Analysis for Precision Medicine

Li, Q. (2017) · UA Campus Repository (The University of Arizona)

Sample size calculations and normalization methods for RNA-seq data.

Li, X. (2017) · doi:10.18297/etd/2856

Bayesian Linear Mixed Models for Motif Activity AnalysisPreprint

Lederer, S., Heskes, T., van Heeringen, S. J., & Albers, C. A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/782615

Nanopore sequencing reveals U2AF1 S34F -associated full-length isoformsPreprint

Soulette, C. M., Hrabeta‐Robinson, E., Tang, A. D., Marin, M. G., & Brooks, A. N. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/871863

Member profile: Dafydd Walters

Walters, D. V. (2013) · Physiology News · doi:10.36866/pn.90.42

The Society’s story: Keeping our history alive

Miller, D. (2013) · Physiology News · doi:10.36866/pn.90.13

Biophysical Society’s 57th Annual Meeting

Howells, S., & Boross-Toby, N. (2013) · Physiology News · doi:10.36866/pn.90.17

Book Review: Essential Guide to Reading Biomedical Papers By Philip D Langton

Miller, D. (2013) · Physiology News · doi:10.36866/pn.90.44

Obituary: Beverly Mackenna (1929 – 2012)

Elder, H. Y. (2013) · Physiology News · doi:10.36866/pn.90.45

The Turkish Physiological Society Meeting 2012

Arellano, R., Carpenter, D. O., Dockray, G. J., Varró, A., Scott, R. H., & Verkhratsky, A. (2013) · Physiology News · doi:10.36866/pn.90.17a

Student access to lecture material: How to avoid copyright pitfalls

Evans, M. (2013) · Physiology News · doi:10.36866/pn.90.10

Is the desensitization of postsynaptic receptor channels relevant?

Papke, D. J., & Grosman, C. F. (2013) · Physiology News · doi:10.36866/pn.90.32

Obituary: William Dryden (1941 – 2012)

Smith, P. J. (2013) · Physiology News · doi:10.36866/pn.90.46

Metabolism & Endocrinology Themed Meeting

Ojha, S., Budge, H., & Symonds, M. E. (2013) · Physiology News · doi:10.36866/pn.90.16

In vivo short courses: Giving students first-hand experience and knowledge

Collis, M., & Apps, R. (2013) · Physiology News · doi:10.36866/pn.90.11

Lab profile: The Clinical Pharmacology Unit, Addenbrooke’s Hospital, Cambridge

Shaikh, L. H. (2013) · Physiology News · doi:10.36866/pn.90.40

Q&A: Physiological Reports

Wray, S., & Kleyman, T. (2013) · Physiology News · doi:10.36866/pn.90.37

Films, muscle, potassium and exercise

Jones, D. A. (2013) · Physiology News · doi:10.36866/pn.90.26

The impact of engaging

Dean, L. G. (2013) · Physiology News · doi:10.36866/pn.90.12

Are we in control of our own health?

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