De novo assembly and analysis of RNA-seq data

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

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

<tt>BLAT</tt> —The <tt>BLAST</tt> -Like Alignment Tool

Kent, W. J. (2002) · Genome Research · doi:10.1101/gr.229202 · Cited by 8,480

Fast and SNP-tolerant detection of complex variants and splicing in short reads

Wu, T. D., & Nacu, S. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq057 · Cited by 1,894

Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs

Guttman, M., Garber, M., Levin, J. Z., Donaghey, J., Robinson, J., Adiconis, X., Fan, L., Koziol, M. J., Gnirke, A., Nusbaum, C., et al. (2010) · Nature Biotechnology · doi:10.1038/nbt.1633 · Cited by 1,253

Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data.

Degner, J. F., Marioni, J. C., Pai, A. A., Pickrell, J. K., Nkadori, E., Gilad, Y., & Pritchard, J. K. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp579 · Cited by 502

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

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 21,039

Genomic and Epigenomic Landscapes of Adult De Novo Acute Myeloid Leukemia

The Cancer Genome Atlas Research Network (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1301689 · Cited by 5,078

Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma

Ally, A., Balasundaram, M., Carlsen, R., Chuah, E., Clarke, A., Dhalla, N., Holt, R. A., Jones, S. J. M., Lee, D., Ma, Y., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.046 · Cited by 2,490

Frequent mutation of histone-modifying genes in non-Hodgkin lymphoma

Morin, R. D., Mendez-Lago, M., Mungall, A. J., Goya, R., Mungall, K. L., Corbett, R. D., Johnson, N. A., Severson, T. M., Chiu, R., Field, M., et al. (2011) · Nature · doi:10.1038/nature10351 · Cited by 1,614

Repetitive DNA and next-generation sequencing: computational challenges and solutions

Treangen, T. J., & Salzberg, S. L. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3117 · Cited by 1,597

Computational methods for transcriptome annotation and quantification using RNA-seq

Garber, M., Grabherr, M. G., Guttman, M., & Trapnell, C. (2011) · Nature Methods · doi:10.1038/nmeth.1613 · Cited by 1,042

RNA Sequencing and Analysis

Kukurba, K. R., & Montgomery, S. B. (2015) · Cold Spring Harbor Protocols · doi:10.1101/pdb.top084970 · Cited by 924

TransRate: reference-free quality assessment of de novo transcriptome assemblies

Smith-Unna, R., Boursnell, C., Patro, R., Hibberd, J. M., & Kelly, S. (2016) · Genome Research · doi:10.1101/gr.196469.115 · Cited by 837

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

deFuse: An Algorithm for Gene Fusion Discovery in Tumor RNA-Seq Data

McPherson, A., Hormozdiari, F., Zayed, A., Giuliany, R., Ha, G., Sun, M. G. F., Griffith, M., Heravi Moussavi, A., Senz, J., Melnyk, N., et al. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001138 · Cited by 557

Sequence assembly demystified

Nagarajan, N., & Pop, M. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3367 · Cited by 474

Metagenomics, Metatranscriptomics, and Metabolomics Approaches for Microbiome Analysis: Supplementary Issue: Bioinformatics Methods and Applications for Big Metagenomics Data

Aguiar-Pulido, V., Huang, W., Suarez-Ulloa, V., Cickovski, T., Mathee, K., & Narasimhan, G. (2016) · Evolutionary Bioinformatics · doi:10.4137/ebo.s36436 · Cited by 499

Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study

Zhao, Q.-Y., Wang, Y., Kong, Y.-M., Luo, D., Li, X., & Hao, P. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-s14-s2 · Cited by 479

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

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., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00050-4 · Cited by 376

Next-generation sequencing in the clinic: Promises and challenges

Xuan, J., Yu, Y., Qing, T., Guo, L., & Shi, L. (2013) · Cancer Letters · doi:10.1016/j.canlet.2012.11.025 · Cited by 344

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

Nookaew, I., Papini, M., Pornputtapong, N., Scalcinati, G., Fagerberg, L., Uhlén, M., & Nielsen, J. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks804 · Cited by 309

Combining metagenomics, metatranscriptomics and viromics to explore novel microbial interactions: towards a systems-level understanding of human microbiome

Bikel, S., Valdez-Lara, A., Cornejo-Granados, F., Rico, K., Canizales-Quinteros, S., Soberón, X., Del Pozo-Yauner, L., & Ochoa-Leyva, A. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2015.06.001 · Cited by 263

Giant lungfish genome elucidates the conquest of land by vertebrates

Meyer, A., Schloissnig, S., Franchini, P., Du, K., Woltering, J. M., Irisarri, I., Wong, W. Y., Nowoshilow, S., Kneitz, S., Kawaguchi, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03198-8 · Cited by 211

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

Dissection of a rapidly evolving wheat resistance gene cluster by long-read genome sequencing accelerated the cloning of Pm69

Li, Y., Wei, Z.-Z., Sela, H., Govta, L., Klymiuk, V., Roychowdhury, R., Chawla, H. S., Ens, J., Wiebe, K., Bocharova, V., et al. (2024) · Plant Communications · doi:10.1016/j.xplc.2023.100646 · Cited by 103

Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes

Johnson, L. K., Alexander, H., & Brown, C. T. (2019) · GigaScience · doi:10.1093/gigascience/giy158 · Cited by 94

Genome-guided transcript assembly by integrative analysis of RNA sequence data

Boley, N., Stoiber, M. H., Booth, B. W., Wan, K. H., Hoskins, R. A., Bickel, P. J., Celniker, S. E., & Brown, J. B. (2014) · Nature Biotechnology · doi:10.1038/nbt.2850 · Cited by 48

Genome-wide microRNA and messenger RNA profiling in rodent liver development implicates mir302b and mir20a in repressing transforming growth factor-beta signaling

Wei, W., Hou, J., Alder, O., Ye, X., Lee, S., Cullum, R., Chu, A., Zhao, Y., Warner, S. M., Knight, D. A., et al. (2013) · Hepatology · doi:10.1002/hep.26252 · Cited by 19