Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis

Sahraeian, Sayed Mohammad Ebrahim; Mohiyuddin, Marghoob; Sebra, Robert; Tilgner, Hagen; Afshar, Pegah T.; Au, Kin Fai; Bani Asadi, Narges; Gerstein, Mark B.; Wong, Wing Hung; Snyder, Michael P.; Schadt, Eric; Lam, Hugo Y. K.

doi:10.1038/s41467-017-00050-4

Cited by 386Genomics and Phylogenetic StudiesSingle-cell and spatial transcriptomicsRNA Research and Splicing

Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis

Open access

Sahraeian, Sayed Mohammad Ebrahim, Mohiyuddin, Marghoob, Sebra, Robert, Tilgner, Hagen, Afshar, Pegah T., Au, Kin Fai, Bani Asadi, Narges, Gerstein, Mark B. +4 more·2017·Nature Communications

DOI 10.1038/s41467-017-00050-4PMC PMC5498581

Open this paper in CoChat — free account Open PDF

Abstract

RNA-sequencing (RNA-seq) is an essential technique for transcriptome studies, hundreds of analysis tools have been developed since it was debuted. Although recent efforts have attempted to assess the latest available tools, they have not evaluated the analysis workflows comprehensively to unleash the power within RNA-seq. Here we conduct an extensive study analysing a broad spectrum of RNA-seq workflows. Surpassing the expression analysis scope, our work also includes assessment of RNA variant-calling, RNA editing and RNA fusion detection techniques. Specifically, we examine both short- and long-read RNA-seq technologies, 39 analysis tools resulting in ~120 combinations, and ~490 analyses involving 15 samples with a variety of germline, cancer and stem cell data sets. We report the performance and propose a comprehensive RNA-seq analysis protocol, named RNACocktail, along with a computational pipeline achieving high accuracy. Validation on different samples reveals that our proposed protocol could help researchers extract more biologically relevant predictions by broad analysis of the transcriptome.

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 · 10.1038/s41467-017-00050-4

Cites

76 of 76 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.

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?

GATK recommendations for variant calling on bulk RNAseq involve removing these regions of the alignments prior to variant calling.
“This has been noted by others in the past, and GATK recommendations for variant calling on bulk RNAseq involve removing these regions of the alignments prior to variant calling23.”
From Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes (2020) · cites this paper as [23]
The Results section (‘Variant calling’; page unavailable) says GATK applies RNA-specific filtering steps to handle splicing in RNA-seq data. It does not say that GATK recommends removing the regions in question from alignments before variant calling, so that specific claim is unsupported. (LLM, conf=0.82)
Experimental — accuracy not yet measured

Self-check

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

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

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106 · see citing works
Trimmomatic: a flexible trimmer for Illumina sequence data
Bolger, A. M., Lohse, M., & Usadel, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu170 · Cited by 65,548 · see citing works
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 · see citing works
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
<tt>edgeR</tt> : a Bioconductor package for differential expression analysis of digital gene expression data
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364 · see citing works
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458 · see citing works
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219 · see citing works
HISAT: a fast spliced aligner with low memory requirements
Kim, D., Langmead, B., & Salzberg, S. L. (2015) · Nature Methods · doi:10.1038/nmeth.3317 · Cited by 24,112 · see citing works
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179 · see citing works
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 20,776 · see citing works
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 · see citing works
StringTie enables improved reconstruction of a transcriptome from RNA-seq reads
Pertea, M., Pertea, G. M., Antonescu, C. M., Chang, T.-C., Mendell, J. T., & Salzberg, S. L. (2015) · Nature Biotechnology · doi:10.1038/nbt.3122 · Cited by 14,118 · see citing works
Salmon provides fast and bias-aware quantification of transcript expression
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017) · Nature Methods · doi:10.1038/nmeth.4197 · Cited by 12,700 · see citing works
TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions.
Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., & Salzberg, S. L. (2013) · Genome Biology · doi:10.1186/gb-2013-14-4-r36 · Cited by 12,668 · see citing works
Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks
Trapnell, C., Roberts, A., Goff, L. A., Pertea, G. M., Kim, D., Kelley, D. R., Pimentel, H., Salzberg, S. L., Rinn, J. L., & Pachter, L. S. (2012) · Nature Protocols · doi:10.1038/nprot.2012.016 · Cited by 11,930 · see citing works
A framework for variation discovery and genotyping using next-generation DNA sequencing data
DePristo, M. A., Banks, E., Poplin, R. E., Garimella, K. V., Maguire, J., Hartl, C., Philippakis, A., del Angel, G., Rivas, M. A., Hanna, M., et al. (2011) · Nature Genetics · doi:10.1038/ng.806 · Cited by 11,216 · see citing works
Near-optimal probabilistic RNA-seq quantification
Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016) · Nature Biotechnology · doi:10.1038/nbt.3519 · Cited by 9,723 · see citing works
Velvet: Algorithms for de novo short read assembly using de Bruijn graphsPreprint
Zerbino, D. R., & Birney, E. (2008) · Genome Research · doi:10.1101/gr.074492.107 · Cited by 9,387 · see citing works
dbSNP: the NCBI database of genetic variation
Sherry, S. T. (2001) · Nucleic Acids Research · doi:10.1093/nar/29.1.308 · Cited by 7,502 · see citing works
GENCODE: The reference human genome annotation for The ENCODE Project
Harrow, J., Frankish, A., Gonzalez, J. M., Tapanari, E., Diekhans, M., Kokocinski, F., Aken, B. L., Barrell, D., Zadissa, A., Searle, S., et al. (2012) · Genome Research · doi:10.1101/gr.135350.111 · Cited by 4,585 · see citing works
Self-citation
Differential analysis of gene regulation at transcript resolution with RNA-seq
Trapnell, C., Hendrickson, D. G., Sauvageau, M., Goff, L., Rinn, J. L., & Pachter, L. (2013) · Nature Biotechnology · doi:10.1038/nbt.2450 · Cited by 3,492 · see citing works
ToppGene Suite for gene list enrichment analysis and candidate gene prioritization
Chen, J., Bardes, E. E., Aronow, B. J., & Jegga, A. G. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp427 · Cited by 2,952 · see citing works
A survey of best practices for RNA-seq data analysis
Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szcześniak, M. W., Gaffney, D. J., Elo, L. L., Zhang, X., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0881-8 · Cited by 2,794 · see citing works
GMAP: A genomic mapping and alignment program for mRNA and EST sequences
Wu, T. D., & Watanabe, C. K. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti310 · Cited by 2,354 · see citing works
The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update
Afgan, E., Baker, D., van den Beek, M., Blankenberg, D. J., Bouvier, D., Čech, M., Chilton, J. M., Clements, D., Coraor, N., Eberhard, C., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw343 · Cited by 2,150 · see citing works
Differential analysis of RNA-seq incorporating quantification uncertainty
Pimentel, H., Bray, N. L., Puente, S., Melsted, P., & Pachter, L. (2017) · Nature Methods · doi:10.1038/nmeth.4324 · Cited by 1,534 · see citing works
Oases:robustde novoRNA-seq assembly across the dynamic range of expression levels
Schulz, M. H., Zerbino, D. R., Vingron, M., & Birney, E. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts094 · Cited by 1,423 · see citing works
dbEST — database for “expressed sequence tags”
Boguski, M. S., Lowe, T. M., & Tolstoshev, C. M. (1993) · Nature Genetics · doi:10.1038/ng0893-332 · Cited by 1,341 · see citing works
Human housekeeping genes, revisited
Eisenberg, E., & Levanon, E. Y. (2013) · Trends in Genetics · doi:10.1016/j.tig.2013.05.010 · Cited by 1,249 · see citing works
Ensembl 2016
Yates, A. D., Akanni, W. A., Amode, M. R., Barrell, D. G., Billis, K., Carvalho‐Silva, D., Cummins, C. A., Clapham, P., Fitzgerald, S. F., Gil, L., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1157 · Cited by 1,246 · see citing works
Streaming fragment assignment for real-time analysis of sequencing experiments
Roberts, A., & Pachter, L. (2013) · Nature Methods · doi:10.1038/nmeth.2251 · Cited by 1,063 · see citing works
De novo assembly and analysis of RNA-seq data
Robertson, G., Schein, J. E., Chiu, R., Corbett, R., Field, M. A., Jackman, S. D., Mungall, K. L., Lee, S. S., Okada, H. M., Qian, J. Q., et al. (2010) · Nature Methods · doi:10.1038/nmeth.1517 · Cited by 998 · see citing works
Hybrid error correction and de novo assembly of single-molecule sequencing reads
Koren, S., Schatz, M. C., Walenz, B. P., Martin, J. V., Howard, J. T., Ganapathy, G., Wang, Z., Rasko, D. A., McCombie, W. R., Jarvis, E. D., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2280 · Cited by 993 · see citing works
A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium
SEQC/MAQC-III Consortium (2014) · Nature Biotechnology · doi:10.1038/nbt.2957 · Cited by 983 · cited 4× · see citing works
“In terms of speed, STARlong was 68× faster than GMAP (Supplementary Table 8), while IDP took around 170 CPU hours to process each sample.”
MethodInfluential
SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads
Xie, Y., Wu, G., Tang, J., Luo, R., Patterson, J., Liu, S., Huang, W., He, G., Gu, S., Li, S., et al. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu077 · Cited by 979 · see citing works
MTML-msBayes: Approximate Bayesian comparative phylogeographic inference from multiple taxa and multiple loci with rate heterogeneity
Huang, W., Takebayashi, N., Qi, Y., & Hickerson, M. J. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-1 · Cited by 952 · see citing works
Assembling large genomes with single-molecule sequencing and locality-sensitive hashing
Berlin, K., Koren, S., Chin, C.-S., Drake, J. P., Landolin, J. M., & Phillippy, A. M. (2015) · Nature Biotechnology · doi:10.1038/nbt.3238 · Cited by 928 · see citing works
Ballgown bridges the gap between transcriptome assembly and expression analysis
Frazee, A. C., Pertea, G. M., Jaffe, A. E., Langmead, B., Salzberg, S. L., & Leek, J. T. (2015) · Nature Biotechnology · doi:10.1038/nbt.3172 · Cited by 853 · see citing works
LoRDEC: accurate and efficient long read error correction
Salmela, L., & Rivals, É. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu538 · Cited by 820 · see citing works
Extensive sequencing of seven human genomes to characterize benchmark reference materials
Zook, J. M., Catoe, D., McDaniel, J., Vang, L., Spies, N., Sidow, A., Weng, Z., Liu, Y., Mason, C. E., Alexander, N., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.25 · Cited by 811 · cited 3× · see citing works
“The accuracy was assessed on NA12878 using the NIST high-con ﬁ dence (HC) calls 51 as the gold standard (Fig.”
MethodInfluentialSelf-citation
TopHat-Fusion: an algorithm for discovery of novel fusion transcripts
Kim, D., & Salzberg, S. L. (2011) · Genome biology · doi:10.1186/gb-2011-12-8-r72 · Cited by 806 · see citing works
The HLA genomic loci map: expression, interaction, diversity and disease
Shiina, T., Hosomichi, K., Inoko, H., & Kulski, J. K. (2009) · Journal of Human Genetics · doi:10.1038/jhg.2008.5 · Cited by 788 · see citing works
Efficient de novo assembly of large genomes using compressed data structuresPreprint
Simpson, J. T., & Durbin, R. M. (2011) · Genome Research · doi:10.1101/gr.126953.111 · Cited by 767 · see citing works
Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data
Rapaport, F., Khanin, R., Liang, Y., Pirun, M., Krek, A., Zumbo, P., Mason, C. E., Socci, N. D., & Betel, D. (2013) · Genome Biology · doi:10.1186/gb-2013-14-9-r95 · Cited by 733 · see citing works
A single-molecule long-read survey of the human transcriptome
Sharon, D., Tilgner, H., Grubert, F., & Snyder, M. (2013) · Nature Biotechnology · doi:10.1038/nbt.2705 · Cited by 701 · see citing works
Self-citation
Assessment of transcript reconstruction methods for RNA-seq
The RGASP Consortium, Steijger, T., Abril, J. F., Engström, P. G., Kokocinski, F., Hubbard, T. J., Guigó, R., Harrow, J., & Bertone, P. (2013) · Nature Methods · doi:10.1038/nmeth.2714 · Cited by 699 · see citing works
RNA Editing Enzyme APOBEC1 and Some of Its Homologs Can Act as DNA Mutators
Harris, R. S., Petersen‐Mahrt, S. K., & Neuberger, M. S. (2002) · Molecular Cell · doi:10.1016/s1097-2765(02)00742-6 · Cited by 604 · see citing works
Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms
Patro, R., Mount, S. M., & Kingsford, C. (2014) · Nature Biotechnology · doi:10.1038/nbt.2862 · Cited by 586 · see citing works
Systematic evaluation of spliced alignment programs for RNA-seq data
The RGASP Consortium, Engström, P. G., Steijger, T., Sipos, B., Grant, G. R., Kahles, A., Rätsch, G., Goldman, N., Hubbard, T. J., Harrow, J., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2722 · Cited by 513 · see citing works
proovread : large-scale high-accuracy PacBio correction through iterative short read consensus
Hackl, T., Hedrich, R., Schultz, J., & Förster, F. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu392 · Cited by 499 · see citing works
Gene expression in human embryonic stem cell lines: unique molecular signature
Bhattacharya, B., Miura, T., Brandenberger, R., Mejido, J., Luo, Y., Yang, A. X., Joshi, B. H., Ginis, I., Thies, R. S., Amit, M., et al. (2004) · Blood · doi:10.1182/blood-2003-09-3314 · Cited by 452 · see citing works
Widespread Polycistronic Transcripts in Fungi Revealed by Single-Molecule mRNA Sequencing
Gordon, S., Tseng, E., Salamov, A. A., Zhang, J., Meng, X., Zhao, Z., Kang, D., Underwood, J. G., Grigoriev, I. V., Figueroa, M., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0132628 · Cited by 403 · see citing works
FusionCatcher – a tool for finding somatic fusion genes in paired-end RNA-sequencing dataPreprint
Nicorici, D., Şatalan, M., Edgren, H., Kangaspeska, S., Murumägi, A., Kallioniemi, O., Virtanen, S. M. M., & Kilkku, O. (2014) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/011650 · Cited by 392 · see citing works
Identifying RNA editing sites using RNA sequencing data alone
Ramaswami, G., Zhang, R., Piskol, R., Keegan, L. P., Deng, P., O’Connell, M. A., & Li, J. B. (2013) · Nature Methods · doi:10.1038/nmeth.2330 · Cited by 362 · see citing works
Accurate identification of A-to-I RNA editing in human by transcriptome sequencingPreprint
Bahn, J. H., Lee, J., Li, G., Greer, C., Peng, G., & Xiao, X. G. (2011) · Genome Research · doi:10.1101/gr.124107.111 · Cited by 347 · see citing works
Characterization of the human ESC transcriptome by hybrid sequencing
Au, K. F., Sebastiano, V., Afshar, P. T., Durruthy, J. D., Lee, L., Williams, B. A., van Bakel, H., Schadt, E. E., Reijo-Pera, R. A., Underwood, J. G., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1320101110 · Cited by 341 · see citing works
Self-citation
Improving PacBio Long Read Accuracy by Short Read Alignment
Au, K. F., Underwood, J. G., Lee, L., & Wong, W. H. (2012) · PLoS ONE · doi:10.1371/journal.pone.0046679 · Cited by 335 · see citing works
Self-citation
Defining a personal, allele-specific, and single-molecule long-read transcriptome
Tilgner, H., Grubert, F., Sharon, D., & Snyder, M. P. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1400447111 · Cited by 266 · see citing works
Self-citation
Comprehensive transcriptome analysis using synthetic long-read sequencing reveals molecular co-association of distant splicing events
Tilgner, H., Jahanbani, F., Blauwkamp, T., Moshrefi, A., Jaeger, E., Chen, F., Harel, I., Bustamante, C. D., Rasmussen, M., & Snyder, M. P. (2015) · Nature Biotechnology · doi:10.1038/nbt.3242 · Cited by 223 · see citing works
Self-citation
SOAPfuse: an algorithm for identifying fusion transcripts from paired-end RNA-Seq data
Jia, W., Qiu, K., He, M., Song, P., Zhou, Q., Zhou, F., Yu, Y., Zhu, D., Nickerson, M. L., Wan, S., et al. (2013) · Genome biology · doi:10.1186/gb-2013-14-2-r12 · Cited by 206 · see citing works
A benchmark for RNA-seq quantification pipelines
Teng, M., Love, M. I., Davis, C., Djebali, S., Dobin, A., Graveley, B. R., Li, S., Mason, C. E., Olson, S., Pervouchine, D. D., et al. (2016) · Genome biology · doi:10.1186/s13059-016-0940-1 · Cited by 189 · see citing works
JAFFA: High sensitivity transcriptome-focused fusion gene detection
Davidson, N., Majewski, I. J., & Oshlack, A. (2015) · Genome Medicine · doi:10.1186/s13073-015-0167-x · Cited by 178 · see citing works
Multi-resolution independent component analysis for high-performance tumor classification and biomarker discovery
Han, H., & Li, X. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-s1-s7 · Cited by 167 · see citing works
Comprehensive evaluation of fusion transcript detection algorithms and a meta-caller to combine top performing methods in paired-end RNA-seq data
Liu, S., Tsai, W.-H., Ding, Y., Chen, R., Fang, Z., Huo, Z., Kim, S., Ma, T., Chang, T., Priedigkeit, N., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1234 · Cited by 163 · see citing works
Genome sequence–independent identification of RNA editing sites
Zhang, Q., & Xiao, X. G. (2015) · Nature Methods · doi:10.1038/nmeth.3314 · Cited by 126 · see citing works
Characterization of fusion genes and the significantly expressed fusion isoforms in breast cancer by hybrid sequencing
Weirather, J. L., Afshar, P. T., Clark, T. A., Tseng, E., Powers, L. S., Underwood, J. G., Zabner, J., Korlach, J., Wong, W. H., & Au, K. F. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv562 · Cited by 123 · see citing works
Self-citation
Sigma: Strain-level inference of genomes from metagenomic analysis for biosurveillance
Ahn, T.-H., Chai, J., & Pan, C. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu641 · Cited by 115 · see citing works
RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes
Srivastava, A., Sarkar, H., Gupta, N., & Patro, R. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw277 · Cited by 106 · see citing works
RNA-Seq Gene Profiling - A Systematic Empirical Comparison
Fonseca, N. A., Marioni, J. C., & Brāzma, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0107026 · Cited by 99 · see citing works
Fusion genes in solid tumors: an emerging target for cancer diagnosis and treatment
Parker, B. C., & Zhang, W. (2013) · 癌症：英文版 · doi:10.5732/cjc.013.10178 · Cited by 96 · see citing works
Benchmark analysis of algorithms for determining and quantifying full-length mRNA splice forms from RNA-seq data
Hayer, K. E., Pizarro, A., Lahens, N. F., Hogenesch, J. B., & Grant, G. R. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv488 · Cited by 93 · cited 7× · see citing works
“On MCF7-100 and MCF7-300 samples, Cuf ﬂ inks-TopHat prediction sets were not enriched in any MCF7 or breast cancer-related gene expression study, while StringTie-HISAT2 ’ s and Salmon-SMEM ’ s top over-expressed genes were highly enriched in many MCF7 and breast cancer cell line-related gene sets (Supplementary Data 1…”
BackgroundMethodInfluential
VarSim: a high-fidelity simulation and validation framework for high-throughput genome sequencing with cancer applications
Mu, J. C., Mohiyuddin, M., Li, J., Asadi, N. B., Gerstein, M., Abyzov, A. S., Wong, W. H., & Lam, H. Y. K. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu828 · Cited by 76 · see citing works
Self-citation
Accurate Identification and Analysis of Human mRNA Isoforms Using Deep Long Read Sequencing
Tilgner, H., Raha, D., Habegger, L., Mohiuddin, M., Gerstein, M., & Snyder, M. P. (2013) · G3 Genes Genomes Genetics · doi:10.1534/g3.112.004812 · Cited by 65 · see citing works
Self-citation
Grape RNA-Seq analysis pipeline environment
Knowles, D. G., Röder, M., Merkel, A., & Guigó, R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt016 · Cited by 34 · see citing works
RASER: reads aligner for SNPs and editing sites of RNA
Ahn, J., & Xiao, X. G. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv505 · Cited by 23 · see citing works
Identification of T1D susceptibility genes within the MHC region by combining protein interaction networks and SNP genotyping data
Brorsson, C., Hansen, N. T., Lage, K., Bergholdt, R., Brunak, S., & Pociot, F. (2009) · Diabetes Obesity and Metabolism · doi:10.1111/j.1463-1326.2008.01004.x · Cited by 21 · see citing works

Flagged first, then by citations

Sorted by citations

RNA sequencing: the teenage years
Stark, R., Grzelak, M., & Hadfield, J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0150-2 · Cited by 1,906 · see citing works
RNA-Seq differential expression analysis: An extended review and a software tool
Silva, J. C., Domingues, D. S., & Lopes, F. M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0190152 · Cited by 624 · see citing works
Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes
Heaton, H., Talman, A. M., Knights, A., Imaz, M., Gaffney, D. J., Durbin, R., Hemberg, M., & Lawniczak, M. K. N. (2020) · Nature Methods · doi:10.1038/s41592-020-0820-1 · Cited by 506 · see citing works
Magic-BLAST, an accurate RNA-seq aligner for long and short reads
Boratyn, G. M., Thierry‐Mieg, J., Thierry‐Mieg, D., Busby, B., & Madden, T. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2996-x · Cited by 340 · see citing works
Interpretation of differential gene expression results of RNA-seq data: review and integration
McDermaid, A., Monier, B., Zhao, J., Liu, B., & Ma, Q. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby067 · Cited by 324 · see citing works
De novo transcriptome assembly: A comprehensive cross-species comparison of short-read RNA-Seq assemblers
Hölzer, M., & Marz, M. (2019) · GigaScience · doi:10.1093/gigascience/giz039 · Cited by 237 · see citing works
Assessing and mitigating batch effects in large-scale omics studies
Yu, Y., Mai, Y., Zheng, Y., & Shi, L. M. (2024) · Genome biology · doi:10.1186/s13059-024-03401-9 · Cited by 231 · see citing works
Towards a comprehensive picture of C-to-U RNA editing sites in angiosperm mitochondria
Edera, A., Gandini, C. L., & Sanchez-Puerta, M. V. (2018) · Plant Molecular Biology · doi:10.1007/s11103-018-0734-9 · Cited by 184 · see citing works
Editorial focus: understanding off-target effects as the key to successful RNAi therapy
Bartoszewski, R., & Sikorski, A. F. (2019) · Cellular & Molecular Biology Letters · doi:10.1186/s11658-019-0196-3 · Cited by 150 · see citing works
UTAP: User-friendly Transcriptome Analysis Pipeline
Kohen, R., Barlev, J., Hornung, G., Stelzer, G., Feldmesser, E., Kogan, K., Safran, M., & Leshkowitz, D. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2728-2 · Cited by 140 · see citing works
Adipogenesis, Osteogenesis, and Chondrogenesis of Human Mesenchymal Stem/Stromal Cells: A Comparative Transcriptome Approach
Robert, A. W., Marcon, B. H., Dallagiovanna, B., & Shigunov, P. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.00561 · Cited by 138 · see citing works
Transcriptomic response to three osmotic stresses in gills of hybrid tilapia (Oreochromis mossambicus female × O. urolepis hornorum male)
Su, H., Ma, D., Zhu, H., Liu, Z., & Gao, F. (2020) · BMC Genomics · doi:10.1186/s12864-020-6512-5 · Cited by 100 · see citing works
Robustness of differential gene expression analysis of RNA-seq
Stupnikov, A., McInerney, C. E., Savage, K. I., McIntosh, S., Emmert‐Streib, F., Kennedy, R. D., Salto‐Tellez, M., Prise, K. M., & McArt, D. G. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.05.040 · Cited by 82 · see citing works
DE-kupl: exhaustive capture of biological variation in RNA-seq data through k-mer decomposition
Audoux, J., Philippe, N., Chikhi, R., Salson, M., Gallopin, M., Gabriel, M., Coz, J. L., Drouineau, E., Commes, T., & Gautheret, D. (2017) · Genome biology · doi:10.1186/s13059-017-1372-2 · Cited by 85 · see citing works
Limitations of alignment-free tools in total RNA-seq quantification
Wu, D. C., Yao, J., Ho, K. S., Lambowitz, A. M., & Wilke, C. O. (2018) · BMC Genomics · doi:10.1186/s12864-018-4869-5 · Cited by 83 · see citing works
Challenges and opportunities for circRNA identification and delivery
Dong, J., Zeng, Z., Huang, Y., Chen, C., Cheng, Z., & Zhu, Q. (2023) · Critical Reviews in Biochemistry and Molecular Biology · doi:10.1080/10409238.2023.2185764 · Cited by 77 · see citing works
Imbalance of Excitatory/Inhibitory Neuron Differentiation in Neurodevelopmental Disorders with an NR2F1 Point Mutation
Zhang, K., Fang, Y., Zhu, J. K., Han, S., Chen, J., Wu, X., Chen, Y., Shen, T., Liao, J., Guo, W., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.03.085 · Cited by 66 · see citing works
Microfluidic isoform sequencing shows widespread splicing coordination in the human transcriptome
Tilgner, H., Jahanbani, F., Gupta, I., Collier, P., Wei, E., Rasmussen, M., & Snyder, M. (2018) · Genome Research · doi:10.1101/gr.230516.117 · Cited by 71 · see citing works
Self-citation
BP4RNAseq: a babysitter package for retrospective and newly generated RNA-seq data analyses using both alignment-based and alignment-free quantification method
Sun, S., Xu, L., Zou, Q., & Wang, G. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa832 · Cited by 71 · see citing works
RASflow: an RNA-Seq analysis workflow with Snakemake
Zhang, X., & Jonassen, I. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3433-x · Cited by 70 · see citing works
RNA-seq assistant: machine learning based methods to identify more transcriptional regulated genes
Wang, L., Xi, Y., Sung, S., & Qiao, H. (2018) · BMC Genomics · doi:10.1186/s12864-018-4932-2 · Cited by 68 · see citing works
Variability in estimated gene expression among commonly used RNA-seq pipelines
Arora, S., Pattwell, S. S., Holland, E. C., & Bolouri, H. (2020) · Scientific Reports · doi:10.1038/s41598-020-59516-z · Cited by 67 · see citing works
Recent advances in cancer fusion transcript detection
Dorney, R., Dhungel, B. P., Rasko, J. E. J., Hebbard, L., & Schmitz, U. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac519 · Cited by 66 · see citing works
A comparison of transcriptome analysis methods with reference genome
Liu, X., Zhao, J., Xue, L., Zhao, T., Ding, W., Han, Y., & Ye, H. (2022) · BMC Genomics · doi:10.1186/s12864-022-08465-0 · Cited by 62 · see citing works
Welcome to the big leaves: Best practices for improving genome annotation in non‐model plant genomes
Vuruputoor, V. S., Monyak, D. L., Fetter, K. C., Webster, C., Bhattarai, A., Shrestha, B., Zaman, S., Bennett, J., McEvoy, S. L., Caballero, M., et al. (2023) · Applications in Plant Sciences · doi:10.1002/aps3.11533 · Cited by 60 · see citing works
Evaluation of variant calling tools for large plant genome re-sequencing
Yao, Z., You, F. M., N’Diaye, A., Knox, R. E., McCartney, C. A., Hiebert, C. W., Pozniak, C. J., & Xu, W. W. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03704-1 · Cited by 59 · see citing works
Longest protein, longest transcript or most expression, for accurate gene reconstruction of transcriptomes?Preprint
Gilbert, D. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/829184 · Cited by 59 · see citing works
Deconvolving Clinically Relevant Cellular Immune Cross-talk from Bulk Gene Expression Using CODEFACS and LIRICS Stratifies Patients with Melanoma to Anti–PD-1 Therapy
Wang, K., Patkar, S., Lee, J. S., Gertz, E. M., Robinson, W., Schischlik, F., Crawford, D. R., Schäffer, A. A., & Ruppin, E. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0887 · Cited by 55 · see citing works
A Putative Plasma Membrane Na+/H+ Antiporter GmSOS1 Is Critical for Salt Stress Tolerance in Glycine max
Zhang, M., Cao, J., Zhang, T., Xu, T., Yang, L., Li, X., Ji, F., Gao, Y., Ali, S., Zhang, Q., et al. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.870695 · Cited by 57 · see citing works
Differential gene expression analysis tools exhibit substandard performance for long non-coding RNA-sequencing data
Assefa, A. T., Paepe, K. D., Everaert, C., Mestdagh, P., Thas, O., & Vandesompele, J. (2018) · Genome biology · doi:10.1186/s13059-018-1466-5 · Cited by 59 · see citing works
Getting the Entire Message: Progress in Isoform Sequencing
Hardwick, S. A., Joglekar, A., Flicek, P., Frankish, A., & Tilgner, H. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00709 · Cited by 56 · see citing works
Self-citation
Reconstruction of the Origin of a Neo-Y Sex Chromosome and Its Evolution in the Spotted Knifejaw, Oplegnathus punctatus
Li, M., Zhang, R., Fan, G., Xu, W.-T., Zhou, Q., Wang, L., Li, W.-S., Pang, Z., Yu, M.-J., Liu, Q., et al. (2021) · Molecular biology and evolution · doi:10.1093/molbev/msab056 · Cited by 47 · see citing works
High Level of Conservation of Mitochondrial RNA Editing Sites Among Four Populus Species
Brenner, W. G., Mäder, M., Müller, N. A., Hoenicka, H., Schroeder, H., Zorn, I., Fladung, M., & Kersten, B. (2019) · G3 Genes Genomes Genetics · doi:10.1534/g3.118.200763 · Cited by 51 · see citing works
Designing and Engineering Methylorubrum extorquens AM1 for Itaconic Acid Production
Lim, C. K., Villada, J. C., Chalifour, A., Duran, M. F., Lu, H., & Lee, P. K. H. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.01027 · Cited by 51 · see citing works
Evaluation of Seven Different RNA-Seq Alignment Tools Based on Experimental Data from the Model Plant Arabidopsis thaliana
Schaarschmidt, S., Fischer, A., Zuther, E., & Hincha, D. K. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21051720 · Cited by 51 · see citing works
Chromosome-level genome assembly of Salvia miltiorrhiza with orange roots uncovers the role of Sm2OGD3 in catalyzing 15,16-dehydrogenation of tanshinones
Pan, X. D., Chang, Y., Li, C., Qiu, X., Cui, X., Meng, F., Zhang, S., Li, X., & Lu, S. (2023) · Horticulture Research · doi:10.1093/hr/uhad069 · Cited by 49 · see citing works
Molecular and genetic analysis of resistance to METI-I acaricides in Iranian populations of the citrus red mite Panonychus citri
Alavijeh, E. S., Khajehali, J., Snoeck, S., Panteleri, R., Ghadamyari, M., Jonckheere, W., Bajda, S., Saalwaechter, C., Geibel, S., Douris, V., et al. (2019) · Pesticide Biochemistry and Physiology · doi:10.1016/j.pestbp.2019.12.009 · Cited by 49 · see citing works
Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction
Li, T., Wu, P.-Y., Phan, J. H., Hassazadeh, H., Jones, W. D., Shi, L. M., Fischer, M., Mason, C. E., Li, S., Xu, J., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-74567-y · Cited by 45 · see citing works
A Temporal PROTAC Cocktail‐Mediated Sequential Degradation of AURKA Abrogates Acute Myeloid Leukemia Stem Cells
Liu, F., Wang, X., Duan, J., Hou, Z., Wu, Z., Liu, L., Lei, H., Huang, D. Y., Ren, Y., Wang, Y., et al. (2022) · Advanced Science · doi:10.1002/advs.202104823 · Cited by 45 · see citing works
Single-cell RNA sequencing in skeletal muscle developmental biology
Cai, C., Yue, Y., & Yue, B. (2023) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2023.114631 · Cited by 43 · see citing works
Sandwich‐Structured Implants to Obstruct Multipath Energy Supply and Trigger Self‐Enhanced Hypoxia‐Initiated Chemotherapy Against Postsurgical Tumor Recurrence and Metastasis
Fang, Y., Luo, X., Xu, Y., Liu, Z., Mintz, R. L., Yu, H., Yu, X., Li, K., Ju, E., Wang, H., et al. (2023) · Advanced Science · doi:10.1002/advs.202300899 · Cited by 41 · see citing works
Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling
Yu, Y., Hou, W., Liu, Y., Wang, H., Dong, L., Mai, Y., Chen, Q., Li, Z., Sun, S., Yang, J., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01867-9 · Cited by 40 · see citing works
IRIS-EDA: An integrated RNA-Seq interpretation system for gene expression data analysis
Monier, B., McDermaid, A., Wang, C., Zhao, J., Miller, A. J., Fennell, A. Y., & Ma, Q. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006792 · Cited by 41 · see citing works
In vitro genome editing rescues parkinsonism phenotypes in induced pluripotent stem cells-derived dopaminergic neurons carrying LRRK2 p.G2019S mutation
Chang, K., Huang, C., Ou‐Yang, C., Ho, C.-H., Lin, H., Hsu, C., Chen, Y., Chou, Y., Chen, Y.-J., Chen, Y., et al. (2021) · Stem Cell Research & Therapy · doi:10.1186/s13287-021-02585-2 · Cited by 39 · see citing works
Selectivity and molecular stress responses to classical and botanical acaricides in the predatory mite Phytoseiulus persimilis Athias‐Henriot ( Acari: Phytoseiidae )
Bajda, S., CLERCQ, P. D., & Leeuwen, T. V. (2021) · Pest Management Science · doi:10.1002/ps.6747 · Cited by 36 · see citing works
Infection by the castrating parasitic nematode Sphaerularia bombi changes gene expression in Bombus terrestris bumblebee queens
Colgan, T. J., Carolan, J. C., Sumner, S., Blaxter, M., & Brown, M. J. F. (2019) · Insect Molecular Biology · doi:10.1111/imb.12618 · Cited by 35 · see citing works
LncDC: a machine learning-based tool for long non-coding RNA detection from RNA-Seq data
Li, M., & Liang, C. (2022) · Scientific Reports · doi:10.1038/s41598-022-22082-7 · Cited by 36 · see citing works
Reference-free inferring of transcriptomic events in cancer cells on single-cell data
Eralp, B., & Sefer, E. (2024) · BMC Cancer · doi:10.1186/s12885-024-12331-5 · Cited by 34 · see citing works
Identification of yellowhorn (Xanthoceras sorbifolium) WRKY transcription factor family and analysis of abiotic stress response model
Liu, Z., Saiyinduleng, Chang, Q., Cheng, C., Zheng, Z., & Song, Y. (2020) · Journal of Forestry Research · doi:10.1007/s11676-020-01134-6 · Cited by 33 · see citing works
Targeting cancer cell plasticity by HDAC inhibition to reverse EBV-induced dedifferentiation in nasopharyngeal carcinoma
Xie, J., Wang, Z., Fan, W., Liu, Y., Liu, F., Wan, X., Liu, M., Wang, X., Zeng, D., Wang, Y., et al. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00702-4 · Cited by 33 · see citing works
Patient-Derived Organoid Serves as a Platform for Personalized Chemotherapy in Advanced Colorectal Cancer Patients
Geevimaan, K., Guo, J.-Y., Shen, C., Jiang, J., Fann, C. S. J., Hwang, M., Shui, J., Lin, H.-T., Wang, M.-J. S., Shih, H.-C., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.883437 · Cited by 33 · see citing works
Transcriptome landscapes of differentially expressed genes related to fat deposits in Nandan-Yao chicken
Cong, X., Sun, T., Yang, Z., Xu, W., Wang, J., Zeng, L., Deng, J., & Yang, X. (2021) · Functional & Integrative Genomics · doi:10.1007/s10142-020-00764-7 · Cited by 32 · see citing works
Assembling and Validating Bioinformatic Pipelines for Next-Generation Sequencing Clinical Assays
SoRelle, J. A., Wachsmann, M., & Cantarel, B. L. (2020) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2019-0476-ra · Cited by 31 · see citing works
Sex-Specific Genomic Region Identification and Molecular Sex Marker Development of Rock Bream (Oplegnathus fasciatus)
Gong, J., Li, B., Zhao, J., Zhou, Z., Ke, Q., Zhu, Q., Xu, D., Zhou, T., & Xu, P. (2022) · Marine Biotechnology · doi:10.1007/s10126-022-10095-2 · Cited by 31 · see citing works
Next-generation sequencing and prenatal 'omics: advanced diagnostics and new insights into human development
Vora, N. L., & Hui, L. (2018) · Genetics in Medicine · doi:10.1038/s41436-018-0087-4 · Cited by 31 · see citing works
Fine particulate matter (PM2.5) induces testosterone disruption by triggering ferroptosis through SIRT1/HIF-1α signaling pathway in male mice
Zheng, S., Jiang, J., Shu, Z., Qiu, C., Jiang, L., Zhao, N., Lin, X., Qian, Y., Liang, B., & Qiu, L. (2024) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2024.05.026 · Cited by 30 · see citing works
The nucleolar protein NOP2 is required for nucleolar maturation and ribosome biogenesis during preimplantation development in mammals
Wang, H., Wang, L., Wang, Z., Dang, Y., Shi, Y., Zhao, P., & Zhang, K. (2019) · The FASEB Journal · doi:10.1096/fj.201902623r · Cited by 28 · see citing works
Prospects and challenges of cancer systems medicine: from genes to disease networks
Karimi, M. R., Karimi, A. H., Abolmaali, S., Sadeghi, M., & Schmitz, U. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab343 · Cited by 33 · see citing works
Overlapping functions of RBBP4 and RBBP7 in regulating cell proliferation and histone H3.3 deposition during mouse preimplantation development
Xiao, L., Dang, Y., Hu, B., Luo, L., Zhao, P., Wang, S., & Zhang, K. (2021) · Epigenetics · doi:10.1080/15592294.2021.1999006 · Cited by 30 · see citing works
Transcriptome-wide association study for postpartum depression implicates altered B-cell activation and insulin resistance
Guintivano, J., Åberg, K. A., Clark, S. L., Rubinow, D. R., Sullivan, P. F., Meltzer-Brody, S. E., & van den Oord, E. J. C. G. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01525-7 · Cited by 28 · see citing works
A real-world multi-center RNA-seq benchmarking study using the Quartet and MAQC reference materials
Wang, D., Liu, Y., Zhang, Y., Chen, Q., Han, Y., Hou, W., Liu, C., Yu, Y., Li, Z., Li, Z., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50420-y · Cited by 28 · see citing works
A full life cycle biological clock based on routine clinical data and its impact in health and diseases
Wang, K., Liu, F., Wu, W., Hu, C., Shen, X., Wang, M., Li, G., Zeng, F., Liu, L., Wong, I. N., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-04006-w · Cited by 35 · see citing works
Targeting spike protein-induced TLR/NET axis by COVID-19 therapeutic NRICM102 ameliorates pulmonary embolism and fibrosis
Wei, W., Liaw, C., Tsai, K.-C., Chiou, C., Tseng, Y.-H., Chiou, W., Lin, Y., Tsai, C.-I., Lin, C.-S., Lin, C., et al. (2022) · Pharmacological Research · doi:10.1016/j.phrs.2022.106424 · Cited by 28 · see citing works
A comprehensive workflow for optimizing RNA-seq data analysis
Gao, J., Zheng, J.-Y., Ren, S.-N., Yin, W., Xia, X., Li, Y., & Wang, H. (2024) · BMC Genomics · doi:10.1186/s12864-024-10414-y · Cited by 28 · see citing works
Serum taurine affects lung cancer progression by regulating tumor immune escape mediated by the immune microenvironment
Liang, T.-L., Pan, H., Yan, P., Mi, J., Liu, X., Bao, W.-Q., Lian, L.-R., Zhang, C., Chen, Y., Wang, J.-R., et al. (2024) · Journal of Advanced Research · doi:10.1016/j.jare.2024.09.005 · Cited by 27 · see citing works
Circ RNA wrap – a flexible pipeline for circ RNA identification, transcript prediction, and abundance estimation
Li, L., Bu, D., & Zhao, Y. C. (2019) · FEBS Letters · doi:10.1002/1873-3468.13423 · Cited by 27 · see citing works
TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data
Conard, A. M., Goodman, N., Hu, Y., Perrimon, N., Singh, R., Lawrence, C. E., & Larschan, E. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab384 · Cited by 27 · see citing works
Chronic paternal morphine exposure increases sensitivity to morphine-derived pain relief in male progeny
Toussaint, A. B., Foster, W., Jones, J. M., Kaufmann, S., Wachira, M., Hughes, R., Bongiovanni, A. R., Famularo, S. T., Dunham, B. P., Schwark, R., et al. (2022) · Science Advances · doi:10.1126/sciadv.abk2425 · Cited by 27 · see citing works
Integration of metabolomics and transcriptomics to reveal ferroptosis is involved in Tripterygium wilfordii polyglycoside tablet-induced testicular injury
Qin, Z., Zhang, G., Jiang, S., Ning, F., Zhao, Z., Huang, M., & Jin, J. (2022) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2022.116055 · Cited by 27 · see citing works
Hologenome analysis reveals independent evolution to chemosymbiosis by deep-sea bivalves
Guo, Y., Meng, L.-F., Wang, M.-X., Zhong, Z.-S., Li, D.-H., Zhang, Y.-L., Li, H., Zhang, H., Seim, I., Li, Y.-L., et al. (2023) · BMC Biology · doi:10.1186/s12915-023-01551-z · Cited by 24 · see citing works
MiR-190a potentially ameliorates postoperative cognitive dysfunction by regulating Tiam1
Liu, Q., Hou, A., Zhang, Y., Guo, Y., Li, J., Yao, Y., Niu, K., Li, H., Ma, Y., & Cao, J. (2019) · BMC Genomics · doi:10.1186/s12864-019-6035-0 · Cited by 27 · see citing works
Streamlined Low-Input Transcriptomics through EASY-RNAseq
Zhou, Y., Xu, H., Wu, H., Yu, H., Zhou, P., Qiu, X., Zheng, Z., Chen, Q., Xu, F., Li, G., et al. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.08.002 · Cited by 26 · see citing works
Microplastic exposure disturbs sleep structure, reduces lifespan, and decreases ovary size in Drosophila melanogaster
Yan, W., 中国科学院深圳先进技术研究院深港脑科学创新研究院, 广. 深. 5., Li, Z.-J., Lin, Z.-Y., Ji, S., Tse, W. K. F., Meng, Z., Liu, C. X., Li, L., 中国科学院深圳先进技术研究院脑认知与脑疾病研究所中国科学院脑联结解析与调控重点实验室, 广. 深. 5., et al. (2024) · 动物学研究 · doi:10.24272/j.issn.2095-8137.2024.038 · Cited by 26 · see citing works
In Search of Biomarkers for Pathogenesis and Control of Leishmaniasis by Global Analyses of Leishmania-Infected Macrophages
Veras, P. S. T., Ramos, P. I. P., & de Menezes, J. P. B. (2018) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2018.00326 · Cited by 26 · see citing works
Sin3a regulates the developmental progression through morula‐to‐blastocyst transition via Hdacl
Zhao, P., Li, S., Wang, H., Dang, Y., Wang, L., Liu, T., Wang, S., Li, X., & Zhang, K. (2019) · The FASEB Journal · doi:10.1096/fj.201901213r · Cited by 26 · see citing works
Genome-wide methylation and expression analyses reveal the epigenetic landscape of immune-related diseases for tobacco smoking
Mao, Y. Q., Huang, P., Wang, Y., Wang, M., Li, M. D., & Yang, Z. (2021) · Clinical Epigenetics · doi:10.1186/s13148-021-01208-0 · Cited by 26 · see citing works
HISAT2 Parallelization Method Based on Spark Cluster
Guo, J., Gao, J., & Liu, Z. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2179/1/012038 · Cited by 26 · see citing works
Fatty acid synthase inhibitor cerulenin hinders liver cancer stem cell properties through FASN/APP axis as novel therapeutic strategies
Chen, L.-Y., Wu, D.-S., & Shen, Y. (2024) · Journal of Lipid Research · doi:10.1016/j.jlr.2024.100660 · Cited by 27 · see citing works
Dynamic Transcriptome Reveals the Mechanism of Liver Injury Caused by DHAV-3 Infection in Pekin Duck
Cao, J., Zhang, Y., Chen, Y., Liang, S., Liu, D., Fan, W., Xu, Y., Liu, H., Zhou, Z., Liu, X., et al. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.568565 · Cited by 25 · see citing works
A de novo Full-Length mRNA Transcriptome Generated From Hybrid-Corrected PacBio Long-Reads Improves the Transcript Annotation and Identifies Thousands of Novel Splice Variants in Atlantic Salmon
Ramberg, S., Høyheim, B., Østbye, T.-K. K., & Andreassen, R. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.656334 · Cited by 25 · see citing works
Exploring the selective vulnerability in Alzheimer disease using tissue specific variant analysis
Dharshini, S. A. P., Taguchi, Y., & Gromiha, M. M. (2018) · Genomics · doi:10.1016/j.ygeno.2018.05.024 · Cited by 25 · see citing works
Analysis of the mechanism of curcumin against osteoarthritis using metabolomics and transcriptomics
Deng, W., He, Q., & Zhang, W. (2023) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/s00210-023-02785-y · Cited by 26 · see citing works
Interrogations of single-cell RNA splicing landscapes with SCASL define new cell identities with physiological relevance
Xiang, X., He, Y., Zhang, Z., & Yang, X. (2024) · Nature Communications · doi:10.1038/s41467-024-46480-9 · Cited by 23 · see citing works
An integrative transcriptomic and genomic analysis reveals novel insights into the hub genes and regulatory networks associated with rubber synthesis in H. brasiliensis
Ding, Z., Fu, L., Deguan, T., Sun, X., & Zhang, J. (2020) · Industrial Crops and Products · doi:10.1016/j.indcrop.2020.112562 · Cited by 23 · see citing works
Transcriptome analysis reveals key genes and pathways associated with egg production in Nandan-Yao domestic chicken
Sun, T., Cong, X., Deng, J., Yang, Z., Xu, W., Zou, L., Du, W., Li, S., Huo, X., Zeng, L., et al. (2021) · Comparative Biochemistry and Physiology Part D Genomics and Proteomics · doi:10.1016/j.cbd.2021.100889 · Cited by 23 · see citing works
Comparative Transcriptomic Analysis of mRNAs, miRNAs and lncRNAs in the Longissimus dorsi Muscles between Fat-Type and Lean-Type Pigs
Zhang, J., Wang, J., Ma, C., Wang, W., Wang, H., & Jiang, Y. (2022) · Biomolecules · doi:10.3390/biom12091294 · Cited by 23 · see citing works
Low sucrose availability reduces basal spikelet fertility by inducing abscisic acid and jasmonic acid synthesis in wheat
Sun, W., Lu, C., Wen, L., Liu, Y., Zhou, X., Xiao, X., Guo, X., Wang, Z., Sun, Z., Zhang, Z., et al. (2023) · Journal of Experimental Botany · doi:10.1093/jxb/erad484 · Cited by 23 · see citing works
PERK13 modulates phosphate deficiency-induced root hair elongation in Arabidopsis
Xue, C., Li, W., Shen, R. F., & Lan, P. (2021) · Plant Science · doi:10.1016/j.plantsci.2021.111060 · Cited by 22 · see citing works
Isolating QTL controlling sugarcane leaf blight resistance using a two-way pseudo-testcross strategy
Wang, Z., Lu, G., Wu, Q., Li, A., Que, Y., & Xu, L. (2021) · The Crop Journal · doi:10.1016/j.cj.2021.11.009 · Cited by 22 · see citing works
Attenuating mitochondrial dysfunction and morphological disruption with PT320 delays dopamine degeneration in MitoPark mice
Wang, V., Tseng, K.-Y., Kuo, T., Huang, E. Y., Lan, K.-L., Chen, Z., Ma, K., Greig, N. H., Jung, Y. J., Choi, H.-I., et al. (2024) · Journal of Biomedical Science · doi:10.1186/s12929-024-01025-6 · Cited by 22 · see citing works
Landscape of the long non-coding RNA transcriptome in human heart
Jiang, C., Ding, N., Li, J., Jin, X., Li, X., Pan, T., Huo, C., Li, Y., Xu, J., & Li, X. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby052 · Cited by 22 · see citing works
Analysis of public RNA-sequencing data reveals biological consequences of genetic heterogeneity in cell line populations
Fasterius, E., & Szigyarto, C. A. (2018) · Scientific Reports · doi:10.1038/s41598-018-29506-3 · Cited by 22 · see citing works
A new quaternary nanohybrid composite electrode for a high-performance supercapacitor
Ensafi, A. A., Ahmadi, N., Rezaei, B., Abdolmaleki, A., & Mahmoudian, M. (2018) · Energy · doi:10.1016/j.energy.2018.09.031 · Cited by 22 · see citing works
An Alu-mediated duplication in NMNAT1, involved in NAD biosynthesis, causes a novel syndrome, SHILCA, affecting multiple tissues and organs
Bedoni, N., Quinodoz, M., Pinelli, M., Cappuccio, G., Torella, A., Nigro, V., Testa, F., Simonelli, F., Program), T. (. U. D., Cortón, M., et al. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa112 · Cited by 22 · see citing works
Plasma-activated water regulated transcriptome gene expression leading to high germination and growth of mung beans
Chou, Y., & Ting, Y. (2023) · Chemical and Biological Technologies in Agriculture · doi:10.1186/s40538-023-00497-2 · Cited by 21 · see citing works
The STING antagonist SN-011 ameliorates cisplatin induced acute kidney injury via suppression of STING/NF-κB-mediated inflammation
Li, Z., Mao, C., Zhao, Y., Zhao, Y., Yi, H., Liu, J., & Liang, J. (2024) · International Immunopharmacology · doi:10.1016/j.intimp.2024.113876 · Cited by 21 · see citing works
Post-stroke Delivery of Valproic Acid Promotes Functional Recovery and Differentially Modifies Responses of Peri-Infarct Microglia
Kuo, T., Chen, Y.-H., Wang, V., Wu, J., & Tseng, K.-Y. (2021) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2021.639145 · Cited by 21 · see citing works
Detection and Analysis of C-to-U RNA Editing in Rice Mitochondria-Encoded ORFs
Zheng, P., Wang, D., Huang, Y., Chen, H., Du, H., & Tu, J. (2020) · Plants · doi:10.3390/plants9101277 · Cited by 20 · see citing works
Dynamic DNA methylation changes reveal tissue-specific gene expression in sugarcane
Xue, Y., Zou, C., Zhang, C., Yu, H., Chen, B., & Wang, H. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.1036764 · Cited by 20 · see citing works
Brain transcriptome of gobies inhabiting natural CO 2 seeps reveal acclimation strategies to long‐term acidification
Suresh, S., Mirasole, A., Ravasi, T., Vizzini, S., & Schunter, C. (2023) · Evolutionary Applications · doi:10.1111/eva.13574 · Cited by 16 · see citing works

A survey of best practices for RNA-seq data analysis
Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szcześniak, M. W., Gaffney, D. J., Elo, L. L., Zhang, X., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0881-8 · Cited by 2,794 · see citing works
Improved RNA‐seq Workflows Using CyVerse Cyberinfrastructure
Chougule, K. M., Wang, L., Stein, J. C., Wang, X., Devisetty, U. K., Klein, R. R., & Ware, D. (2018) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.53 · Cited by 4 · see citing works
RNASeqR: An R Package for Automated Two-Group RNA-Seq Analysis Workflow
Chao, K.-H., Hsiao, Y.-W., Lee, Y.-F., Lee, C., Lai, L., Tsai, M., Lu, T., & Chuang, E. Y.-Y. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2956708 · Cited by 16 · see citing works
Abstract 4867: Comparative analysis of RNA sequencing methods for characterization of cancer transcriptomics
Abo, R., Lin, L., Hunter, S. S., Dolcen, D. N., Paquette, R. R., Laing, A., de Waal, L. M., Thorner, A. R., Ducar, M. D., Ziaugra, L., et al. (2015) · Cancer Research · doi:10.1158/1538-7445.am2015-4867 · Cited by 1 · see citing works
Data Analysis Pipeline for RNA‐seq Experiments: From Differential Expression to Cryptic Splicing
Yalamanchili, H. K., Wan, Y., & Liu, Z. (2017) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.33 · Cited by 51 · see citing works

Authors: Sahraeian, S. M. E., Mohiyuddin, M., Sebra, R., Tilgner, H., Afshar, P. T., Au, K. F., Bani Asadi, N., Gerstein, M. B., Wong, W. H., Snyder, M. P., Schadt, E., & Lam, H. Y. K.
Affiliations: Icahn School of Medicine at Mount Sinai (US)
Stanford Medicine (US)
Stanford University (US)
University of Iowa (US)
Yale University (US)
Corresponding author: Lam, Hugo Y. K.
ORCID: Sebra, Robert
Tilgner, Hagen
Au, Kin Fai
Gerstein, Mark B.
Wong, Wing Hung
Snyder, Michael P.
Schadt, Eric
Lam, Hugo Y. K.
Venue: Nature Communications
Publisher: Springer Science and Business Media LLC
Published: 2017-07-05
Volume: 8
Issue: 1
Pages: 59-59
ISSN: 2041-1723 (ISSN-L)
DOI: 10.1038/s41467-017-00050-4
PMC: PMC5498581
OpenAlex: W2731748842
Cited by: 376
References: 76
Citations by year: 2026: 29 · 2025: 48 · 2024: 64 · 2023: 41 · 2022: 49 · 2021: 60 · 2020: 28 · 2019: 31 · 2018: 20 · 2017: 5 · 2014: 1
FWCI: 9.38
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Nature Communications (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: National Institutes of Health
Genentech
National Human Genome Research Institute (R01HG008759, P50HG007735, R01HG007834)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
Keywords: gene expression profiling, RNA editing, computational pipeline, tumor transcriptome
MeSH terms: Base Sequence, Cell Line, Humans, Embryonic Stem Cells, Transcriptome

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

Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Self-check

Cites

Cited by

Related

Record

About these checks