A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae

The Sequence Alignment/Map format and SAMtools

Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041

Fast and accurate short read alignment with Burrows-Wheeler transform

Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607

<tt>edgeR</tt> : a Bioconductor package for differential expression analysis of digital gene expression data

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The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data

McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895

BEDTools: a flexible suite of utilities for comparing genomic features

Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437

Full-length transcriptome assembly from RNA-Seq data without a reference genome

Grabherr, M. G., Haas, B. J., Yassour, M., Levin, J. Z., Thompson, D. A., Amit, I., Adiconis, X., Fan, L., Raychowdhury, R., Zeng, Q., et al. (2011) · Nature Biotechnology · doi:10.1038/nbt.1883 · Cited by 21,201

Differential expression analysis for sequence count data

Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581

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

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

Impaired response of the bronchial epithelium to inflammation characterizes severe equine asthma

Tessier, L., Côté, O., Clark, M. E., Viel, L., Diaz-Méndez, A., Anders, S., & Bienzle, D. (2017) · BMC Genomics · doi:10.1186/s12864-017-4107-6 · Cited by 43

Established and Upcoming Yeast Expression Systems

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Integrative RNA-seq and microarray data analysis reveals GC content and gene length biases in the psoriasis transcriptome

Swindell, W. R., Xing, X., Voorhees, J. J., Elder, J. T., Johnston, A., & Gudjonsson, J. E. (2014) · Physiological Genomics · doi:10.1152/physiolgenomics.00022.2014 · Cited by 42

Transcriptome Analysis Comparison of Lipid Biosynthesis in the Leaves and Developing Seeds of Brassica napus

Chen, J., Tan, R.-K., Guo, X., Fu, Z.-L., Wang, Z., Zhang, Z., & Tan, X. (2015) · PLoS ONE · doi:10.1371/journal.pone.0126250 · Cited by 42

Comparative transcriptomics reveals striking similarities between the bovine and feline isolates of Tritrichomonas foetus: consequences for in silico drug-target identification

Morin-Adeline, V., Lomas, R., O’Meally, D., Stack, C. M., Conesa, A., & Šlapeta, J. R. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-955 · Cited by 40

Current analysis of host–parasite interactions with a focus on next generation sequencing data

Greenwood, J. M., Ezquerra, A. L., Behrens, S., Branca, A., & Mallet, L. (2016) · Zoology · doi:10.1016/j.zool.2016.06.010 · Cited by 37

The parasite Trichomonas vaginalis expresses thousands of pseudogenes and long non-coding RNAs independently from functional neighbouring genes

Woehle, C., Kusdian, G., Radine, C., Graur, D., Landan, G., & Gould, S. B. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-906 · Cited by 38

Functional analysis and transcriptional regulation of two orthologs ofARO10, encoding broad-substrate-specificity 2-oxo-acid decarboxylases, in the brewing yeastSaccharomyces pastorianusCBS1483

Bolat, I., Romagnoli, G., Zhu, F., Pronk, J. T., & Daran, J.-M. G. (2013) · FEMS Yeast Research · doi:10.1111/1567-1364.12051 · Cited by 39

The activation of the NF-κB-JNK pathway is independent of the PI3K-Rac1-JNK pathway involved in the bFGF-regulated human fibroblast cell migration

Xuan, Y., Chi, L., Tian, H., Cai, W., Sun, C. M., Wang, T., Zhou, X., Shao, M., Zhu, Y., Niu, C. S., et al. (2016) · Journal of Dermatological Science · doi:10.1016/j.jdermsci.2016.01.003 · Cited by 38

COLOMBOS v2.0: an ever expanding collection of bacterial expression compendia: Table 1.

Meysman, P., Sonego, P., Bianco, L., Fu, Q., Ledezma-Tejeida, D., Gama‐Castro, S., Liebens, V., Michiels, J. J., Laukens, K., Kathleen Marchal, D., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1086 · Cited by 38

De novo Comparative Transcriptome Analysis of Genes Differentially Expressed in the Scion of Homografted and Heterografted Tomato Seedlings

Wang, H., Zhou, P., Zhu, W., & Wang, F. (2019) · Scientific Reports · doi:10.1038/s41598-019-56563-z · Cited by 38

The integrated analyses of metabolomics and transcriptomics in gill of GIFT tilapia in response to long term salinity challenge

Qin, H., Yu, Z., Zhu, Z.-X., Lin, Y., Xia, J., & Jia, Y. (2021) · Aquaculture and Fisheries · doi:10.1016/j.aaf.2021.02.006 · Cited by 36

Transcriptomic Signature of the SHATTERPROOF2 Expression Domain Reveals the Meristematic Nature of Arabidopsis Gynoecial Medial Domain

Villarino, G., Flores‐Vergara, M. A., Hu, Q., Sehra, B., Robles, L. M., Brumós, J., Stepanova, A. N., Manrique, S., Colombo, L., Sundberg, E., et al. (2016) · PLANT PHYSIOLOGY · doi:10.1104/pp.15.01845 · Cited by 35

The Genetic Regulation of Secondary Metabolic Pathways in Response to Salinity and Drought as Abiotic Stresses

Qari, S. H., & Tarbiyyah, I. (2021) · Applied Sciences · doi:10.3390/app11156668 · Cited by 35

Towards Improving Skin Cancer Diagnosis by Integrating Microarray and RNA-Seq Datasets

Gálvez, J. M., Castillo-Secilla, D., Herrera, L. J., Valenzuela, O., Caba, O., Prados, J. C., Ortuño, F. M., & Rojas, I. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2953978 · Cited by 38

Improving drug response prediction via integrating gene relationships with deep learning

Li, P., Jiang, Z., Liu, T., Liu, X., Qiao, H., & Yao, X. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae153 · Cited by 36

Genomic limitations to RNA sequencing expression profiling

Hirsch, C. D., Springer, N. M., & Hirsch, C. N. (2015) · The Plant Journal · doi:10.1111/tpj.13014 · Cited by 32

Glycerol stress in S accharomyces cerevisiae : Cellular responses and evolved adaptations

Mattenberger, F., Sabater‐Muñoz, B., Hallsworth, J. E., & Fares, M. A. (2016) · Environmental Microbiology · doi:10.1111/1462-2920.13603 · Cited by 33

Global and targeted approaches to single-cell transcriptome characterization

Kolodziejczyk, A. A., & Lönnberg, T. (2017) · Briefings in Functional Genomics · doi:10.1093/bfgp/elx025 · Cited by 34

Transcriptome analysis reveals the oxidative stress response in Saccharomyces cerevisiae

Zhao, H., Chen, J., Liu, J., & Han, B. (2015) · RSC Advances · doi:10.1039/c4ra14600j · Cited by 33

Overexpression of a pathogenesis-related gene NbHIN1 confers resistance to Tobacco Mosaic Virus in Nicotiana benthamiana by potentially activating the jasmonic acid signaling pathway

Peng, H., Pu, Y., Yang, X., Wu, G., Qing, L., Ma, L., & Sun, X. (2019) · Plant Science · doi:10.1016/j.plantsci.2019.02.018 · Cited by 31

Effective CRISPR interference of an endogenous gene via a single transgene in mice

MacLeod, R. S., Cawley, K. M., Gubrij, I. B., Nookaew, I., Onal, M., & O’Brien, C. A. (2019) · Scientific Reports · doi:10.1038/s41598-019-53611-6 · Cited by 31

Signatures of natural selection in abiotic stress-responsive genes ofSolanum chilense

Böndel, K. B., Nosenko, T., & Stephan, W. (2018) · Royal Society Open Science · doi:10.1098/rsos.171198 · Cited by 30

De novo transcriptome assembly, functional annotation, and expression profiling of rye (Secale cereale L.) hybrids inoculated with ergot (Claviceps purpurea)

Mahmood, K., Orabi, J., Kristensen, P. S., Sarup, P. M., Jørgensen, L. N., & Jahoor, A. (2020) · Scientific Reports · doi:10.1038/s41598-020-70406-2 · Cited by 30

Transcriptional Regulation of Plant Biomass Degradation and Carbohydrate Utilization Genes in the Extreme Thermophile Caldicellulosiruptor bescii

Rodionov, D. A., Rodionova, I. A., Родионов, В. А., Arzamasov, A. A., Zhang, K., Rubinstein, G. M., Tanwee, T. N. N., Bing, R. G., Crosby, J. R., Nookaew, I., et al. (2021) · mSystems · doi:10.1128/msystems.01345-20 · Cited by 27

Variability in Metagenomic Count Data and Its Influence on the Identification of Differentially Abundant Genes

Jönsson, V., Österlund, T., Nerman, O., & Kristiansson, E. (2016) · Journal of Computational Biology · doi:10.1089/cmb.2016.0180 · Cited by 29

Comprehensive evaluation of RNA-seq analysis pipelines in diploid and polyploid species

Payá‐Milans, M., Olmstead, J. W., Nunez, G. H., Rinehart, T. A., & Staton, M. E. (2018) · GigaScience · doi:10.1093/gigascience/giy132 · Cited by 30

Candida glabrata : new tools and technologies—expanding the toolkit

Ho, H.-L., & Haynes, K. (2015) · FEMS Yeast Research · doi:10.1093/femsyr/fov066 · Cited by 29

The functional genetic link of NLGN4X knockdown and neurodevelopment in neural stem cells

Shi, L., Chang, X., Zhang, P., Coba, M. P., Lu, W., & Wang, K. (2013) · Human Molecular Genetics · doi:10.1093/hmg/ddt226 · Cited by 29

Identification by comparative transcriptomics of core regulatory genes for higher alcohol production in a top-fermenting yeast at different temperatures in beer fermentation

Sun, Z., Wang, M., Wang, Y., Xing, S. J., Hong, K., Chen, Y., Guo, X.-W., & Xiao, D. (2019) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-019-09807-x · Cited by 29

Activation of Piezo1 downregulates renin in juxtaglomerular cells and contributes to blood pressure homeostasis

Yang, X., Zeng, H., Wang, L., Luo, S., & Zhou, Y. (2022) · Cell & Bioscience · doi:10.1186/s13578-022-00931-2 · Cited by 29

Reversal of the diabetic bone signature with anabolic therapies in mice

Marino, S., Akel, N. S., Li, S., Cregor, M. D., Jones, M., Perez, B., Troncoso, G., Meeks, J., Stewart, S., Sato, A. Y., et al. (2023) · Bone Research · doi:10.1038/s41413-023-00261-0 · Cited by 30

Changes in transcript abundance for cuticular proteins and other genes three hours after a blood meal in Anopheles gambiae

Vannini, L., Dunn, W. A., Reed, T. W., & Willis, J. H. (2013) · Insect Biochemistry and Molecular Biology · doi:10.1016/j.ibmb.2013.11.002 · Cited by 29

Transcriptional Profile of Mycobacterium tuberculosis in an in vitro Model of Intraocular Tuberculosis

Abhishek, S., Saikia, U. N., Gupta, A., Bansal, R., Gupta, V., Singh, N., Laal, S., & Verma, I. (2018) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2018.00330 · Cited by 28

Zeta potential changes of Saccharomyces cerevisiae during fermentative and respiratory cycles

Lavaisse, L. M., Hollmann, A., Nazareno, M. A., & Disalvo, E. A. (2018) · Colloids and Surfaces B Biointerfaces · doi:10.1016/j.colsurfb.2018.11.001 · Cited by 28

Exploring candidate genes in a major QTL region associated with salinity tolerance in the skin of Nile tilapia based on transcriptomic analysis

Qin, H., Zhu, Z. X., Lin, H., Xia, J., & Jia, Y. X. (2020) · Aquaculture · doi:10.1016/j.aquaculture.2020.735380 · Cited by 27

Piezo1 stimulates mitochondrial function via cAMP signaling

Li, X., Kordsmeier, J., Nookaew, I., Kim, H., & Xiong, J. (2022) · The FASEB Journal · doi:10.1096/fj.202200300r · Cited by 27

A Comparison of transgenic and wild type soybean seeds: analysis of transcriptome profiles using RNA-Seq

Lambirth, K., Whaley, A. M., Blakley, I., Schlueter, J. A., Bost, K. L., Loraine, A. E., & Piller, K. J. (2015) · BMC Biotechnology · doi:10.1186/s12896-015-0207-z · Cited by 27

Omics/systems biology and cancer cachexia

Gallagher, I. J., Jacobi, C., Tardif, N., Rooyackers, O., & Fearon, K. (2016) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2015.12.022 · Cited by 28

A Whole-Transcriptome Approach to Evaluating Reference Genes for Quantitative Gene Expression Studies: A Case Study in Mimulus

Stanton, K. A., Edger, P. P., Puzey, J. R., Kinser, T. J., Cheng, P. A., Vernon, D. M., Forsthoefel, N. R., & Cooley, A. M. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.116.038075 · Cited by 27

Transcriptomics

Milward, E. A., Shahandeh, A., Heidari, M., Johnstone, D. M., Daneshi, N., & Hondermarck, H. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-394447-4.40029-5 · Cited by 32

Assessment of the Impact of Using a Reference Transcriptome in Mapping Short RNA-Seq Reads

Zhao, S. (2014) · PLoS ONE · doi:10.1371/journal.pone.0101374 · Cited by 28

MOOMIN – Mathematical explOration of ’Omics data on a MetabolIc Network

Pusa, T., Ferrarini, M. G., Andrade, R., Mary, A., Marchetti-Spaccamela, A., Stougie, L., & Sagot, M. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz584 · Cited by 24

Here We Are, But Where Do We Go? A Systematic Review of Crustacean Transcriptomic Studies from 2014–2015

Havird, J. C., & Santos, S. R. (2016) · Integrative and Comparative Biology · doi:10.1093/icb/icw061 · Cited by 22

Stormbow: A Cloud-Based Tool for Reads Mapping and Expression Quantification in Large-Scale RNA-Seq Studies

Zhao, S., Prenger, K., & Smith, L. (2013) · ISRN Bioinformatics · doi:10.1155/2013/481545 · Cited by 21

Decoding the Epitranscriptional Landscape from Native RNA SequencesPreprint

Wongsurawat, T., Jenjaroenpun, P., Wassenaar, T. M., Wadley, T. D., Wanchai, V., Akel, N. S., Franco, A. T., Jennings, M. L., Ussery, D. W., & Nookaew, I. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/487819 · Cited by 21

Multiclass classification for skin cancer profiling based on the integration of heterogeneous gene expression series

Gálvez, J. M., Castillo-Secilla, D., Herrera, L. J., Román, B. S., Valenzuela, O., Ortuño, F. M., & Rojas, I. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196836 · Cited by 20

In Silico Prediction of Large-Scale Microbial Production Performance: Constraints for Getting Proper Data-Driven Models

Zieringer, J., & Takors, R. (2018) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2018.06.002 · Cited by 20

Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing

Johnson, T. S., Xiang, S., Helm, B. R., Abrams, Z. B., Neidecker, P., Machiraju, R., Zhang, Y., Huang, K., & Zhang, J. (2020) · Scientific Reports · doi:10.1038/s41598-020-74917-w · Cited by 23

Systems Biology and immune aging

O’Connor, J., Herrera, G., Martínez‐Romero, A., de Oyanguren, F. S., Díaz, L., Carvalho‐Gomes, Â., Balaguer, S., & Callaghan, R. C. (2014) · Immunology Letters · doi:10.1016/j.imlet.2014.09.009 · Cited by 24

Transcriptome analysis of Plantago major as a phytoremediator to identify some genes related to cypermethrin detoxification

Aioub, A. A. A., Zuo, Y., Li, Y., Qie, X., Zhang, X., Essmat, N., Wu, W., & Hu, Z. (2020) · Environmental Science and Pollution Research · doi:10.1007/s11356-020-10774-4 · Cited by 20

A semi-parametric statistical model for integrating gene expression profiles across different platforms

Lyu, Y., & Li, Q. (2016) · BMC Bioinformatics · doi:10.1186/s12859-015-0847-y · Cited by 21

Probe Region Expression Estimation for RNA-Seq Data for Improved Microarray Comparability

Uziela, K., & Honkela, A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0126545 · Cited by 18

Identifying Glioblastoma Gene Networks Based on Hypergeometric Test Analysis

Stathias, V., Pastori, C., Griffin, T. Z., Komotar, R. J., Clarke, J., Zhang, M., & Ayad, N. G. (2014) · PLoS ONE · doi:10.1371/journal.pone.0115842 · Cited by 18

Aberrantly expressed microRNAs in the context of bladder tumorigenesis

Lee, J., Ryu, D.-S., Kim, W., & Kim, S. (2016) · Korean journal of urology · doi:10.4111/icu.2016.57.s1.s52 · Cited by 18

Compendiums of cancer transcriptomes for machine learning applications

Lim, S. B., Tan, S. J., Lim, W., & Lim, C. T. (2019) · Scientific Data · doi:10.1038/s41597-019-0207-2 · Cited by 18

Transcriptomics: illuminating the molecular landscape of vegetable crops: a review

P, P., V, Bidaramali, V., Lata, S., & Yadav, R. K. (2024) · Journal of Plant Biochemistry and Biotechnology · doi:10.1007/s13562-023-00871-z · Cited by 17

A comparison of microRNA sequencing reproducibility and noise reduction using mirVana and TRIzol isolation methods

Guo, Y. P., Bosompem, A., Zhong, X. Y., Clark, T. A., Shyr, Y., & Kim, A. S. (2014) · International Journal of Computational Biology and Drug Design · doi:10.1504/ijcbdd.2014.061642 · Cited by 17

Computational challenges, tools, and resources for analyzing co‐ and post‐transcriptional events in high throughput

Bahrami‐Samani, E., Vo, D. T., Araujo, P. R. D., Vogel, C., Smith, A. D., Penalva, L. O. F., & Uren, P. J. (2014) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.1274 · Cited by 15

The mechanism of improved intracellular organic selenium and glutathione contents in selenium-enriched Candida utilis by acid stress

Zhang, G., Wang, D., Wang, D., & Wei, G. (2016) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-016-8016-4 · Cited by 17

RNA-Seq analysis on chicken taste sensory organs: An ideal system to study organogenesis

Cui, X., Marshall, B., Shi, N., Chen, S., Romdhane Rekaya, A., & Liu, H. (2017) · Scientific Reports · doi:10.1038/s41598-017-09299-7 · Cited by 17

Deciphering Transcriptional Programming during Pod and Seed Development Using RNA-Seq in Pigeonpea (Cajanus cajan)

Pazhamala, L. T., Agarwal, G., Bajaj, P., Kumar, V. S., Kulshreshtha, A., Saxena, R. K., & Varshney, R. K. (2016) · PLoS ONE · doi:10.1371/journal.pone.0164959 · Cited by 17

Challenges and prospects in utilizing technologies for gene fusion analysis in cancer diagnostics

Su, X., Zheng, Q., Xiu, X., Zhao, Q., Wang, Y., Han, D. W., & Song, P. (2024) · Med-X · doi:10.1007/s44258-024-00033-3 · Cited by 17

Effects of Peptide C12-OOWW-NH2 on Transcriptome and Cell Wall of the Postharvest Fungal Pathogen Penicillium digitatum

Li, X., Feng, G., Wang, W., Yi, L., Deng, L., & Zeng, K. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.574882 · Cited by 16

Transcriptomic and metabolomic responses induced in the livers of growing pigs by a short-term intravenous infusion of sodium butyrate

Ren, E., Chen, X., Yu, S., Xu, J., Su, Y., & Zhu, W. (2018) · animal · doi:10.1017/s1751731118000174 · Cited by 16

Antifungal mechanism of volatile compounds emitted by Actinomycetota Paenarthrobacter ureafaciens from a disease-suppressive soil on Saccharomyces cerevisiae

Nguyen, T.-P., Meng, D.-R., Chang, C., Su, P., Ou, C.-A., Hou, P.-F., Sung, H., Chou, C., Ohme‐Takagi, M., & Huang, H.-J. (2023) · mSphere · doi:10.1128/msphere.00324-23 · Cited by 15

Mapping Human Uterine Disorders Through Single-Cell Transcriptomics

Boldu-Fernández, S., Lliberos, C., Simón, C., & Mas, A. (2025) · Cells · doi:10.3390/cells14030156 · Cited by 18

The use of systems biology in chemical risk assessment

Aguayo‐Orozco, A., Taboureau, O., & Brunak, S. (2019) · Current Opinion in Toxicology · doi:10.1016/j.cotox.2019.03.003 · Cited by 15

Transcriptome Analyses of Candida albicans Biofilms, Exposed to Arachidonic Acid and Fluconazole, Indicates Potential Drug Targets

Kuloyo, O. O., Fourie, R., Cason, E. D., Albertyn, J., & Pohl, C. H. (2020) · G3 Genes Genomes Genetics · doi:10.1534/g3.120.401340 · Cited by 15

DNA adenine methylase, not the PstI restriction-modification system, regulates virulence gene expression in Shiga toxin-producing Escherichia coli

Carter, M. Q., Pham, A., Huynh, S., Parker, C. T., Miller, A., He, X., Hu, B., & Chain, P. (2020) · Food Microbiology · doi:10.1016/j.fm.2020.103722 · Cited by 15

Regulation by sugar and hormone signaling of the growth of Brassica napus L. axillary buds at the transcriptome level

Li, Z., Ding, Y., Xie, L., Jian, H., Gao, Y., Yin, J., Li, J., & Liu, L. (2020) · Plant Growth Regulation · doi:10.1007/s10725-020-00581-9 · Cited by 14

Identification of Changes in Gene expression of rats after Sensory and Motor Nerves Injury

Wang, Y., Guo, Z., Sun, X., Lu, S., Xu, W., Zhao, Q., & Peng, J. C. (2016) · Scientific Reports · doi:10.1038/srep26579 · Cited by 14

Statistical Data Analysis of Microbiomes and Metabolomics

Xia, Y., & Sun, J. (2021) · ACS in focus · doi:10.1021/acsinfocus.7e5035 · Cited by 15

The importance of study design for detecting differentially abundant features in high-throughput experiments

Luo, H., Li, J., Chia, B. K. H., Robson, P., & Nagarajan, N. (2014) · Genome biology · doi:10.1186/s13059-014-0527-7 · Cited by 14

Production of mannosylglycerate in Saccharomyces cerevisiae by metabolic engineering and bioprocess optimization

Faria, C., Borges, N., Rocha, I., & Santos, H. (2018) · Microbial Cell Factories · doi:10.1186/s12934-018-1023-7 · Cited by 13

Deep sequencing analysis of the Kineococcus radiotolerans transcriptome in response to ionizing radiation

Li, L., Chen, Z., Ding, X., Zhan, S., Liu, L., & Guo, J. (2014) · Microbiological Research · doi:10.1016/j.micres.2014.10.003 · Cited by 13

Comparative transcriptomic analysis of surf clams (Paphia undulate) infected with two strains of Vibrio spp. reveals the identity of key immune genes involved in host defense

Yu, M., Zheng, L., Wang, X., Wu, M.-F., Qi, M., Fu, W., & Zhang, Y. (2019) · BMC Genomics · doi:10.1186/s12864-019-6351-4 · Cited by 13

ROSE: A Deep Learning Based Framework for Predicting Ribosome StallingPreprint

Zhang, S., Hu, H., Zhou, J., He, X., Jiang, T., & Zeng, J. (2018) · SSRN Electronic Journal · doi:10.1101/067108 · Cited by 13

Exploring potential therapeutic agents for lipopolysaccharide-induced septic cardiomyopathy based on transcriptomics using bioinformatics

Feng, S., Cai, K., Lin, S., Chen, X., Luo, Y., Wang, J., Lian, G., Lin, Z., & Xie, L. (2023) · Scientific Reports · doi:10.1038/s41598-023-47699-0 · Cited by 12

Quantitative Assessment of Intracellular Effectors and Cellular Response in RAGE Activation

Deepu, V., Rai, V., & Agrawal, D. K. (2024) · Archives of Internal Medicine Research · doi:10.26502/aimr.0168 · Cited by 14

Integration of transcriptomic data identifies key hallmark genes in hypertrophic cardiomyopathy

Xu, J., Liu, X., & Dai, Q. (2021) · BMC Cardiovascular Disorders · doi:10.1186/s12872-021-02147-7 · Cited by 12

Global alterations of the transcriptional landscape during yeast growth and development in the absence of Ume6-dependent chromatin modification

Lardenois, A., Becker, E., Walther, T., Law, M. J., Xie, B., Demougin, P., Strich, R., & Primig, M. (2015) · Molecular Genetics and Genomics · doi:10.1007/s00438-015-1051-5 · Cited by 12

Excess False Positive Rates in Methods for Differential Gene Expression Analysis using RNA-Seq DataPreprint

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Phenotype-specific estimation of metabolic fluxes using gene expression data

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