The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
fastp: an ultra-fast all-in-one FASTQ preprocessor
Open accessPublished version of a preprint
Abstract
Motivation: Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results: We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation: The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.
Overview
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
Cites
Statistics
Verification
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?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“generating ~117.3 Gb of high-quality 150 bp paired-end reads”
From A complete telomere-to-telomere chromosome-level genome assembly of X-ray tetra (Pristella maxillaris) (2025) · cites this paper as [18]
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.
Citations
Cites
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
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
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
Cutadapt removes adapter sequences from high-throughput sequencing reads
Martin, M. (2011) · EMBnet.journal · doi:10.14806/ej.17.1.200 · Cited by 33,889
Detecting ultralow-frequency mutations by Duplex Sequencing
Kennedy, S. R., Schmitt, M. W., Fox, E. J., Kohrn, B. F., Salk, J. J., Ahn, E. H., Prindle, M. J., Kuong, K. J., Shen, J.-C., Risques, R.-A., et al. (2014) · Nature Protocols · doi:10.1038/nprot.2014.170 · Cited by 503 · cited 2×
“It has been adopted by various sequencing methods such as Duplex-Seq (Kennedy et al., 2014) and iDES (Newman et al., 2016).”
Method
Flagged first, then by citations
Research works printed but not linked: 7
- Bianchi, 2015, Noninvasive prenatal testing and incidental detection of occult maternal malignancies, JAMA, 314, 162, 10.1001/jama.2015.7120 · DOI
- Chen, 2017, AfterQC: automatic filtering, trimming, error removing and quality control for fastq data, BMC Bioinformatics, 18, 80, 10.1186/s12859-017-1469-3 · DOI
- Chen, 2018, SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data, Gigascience, 7, 1, 10.1093/gigascience/gix120 · DOI
- Chiang, 2015, SpeedSeq: ultra-fast personal genome analysis and interpretation, Nat. Methods, 12, 966, 10.1038/nmeth.3505 · DOI
- Esposito, 2017, The emerging role of “Liquid Biopsies,” circulating tumor cells, and circulating cell-free tumor dna in lung cancer diagnosis and identification of resistance mutations, Curr. Oncol. Rep., 19, 10.1007/s11912-017-0564-y · DOI
- Newman, 2016, Integrated digital error suppression for improved detection of circulating tumor DNA, Nat. Biotechnol., 34, 547, 10.1038/nbt.3520 · DOI
- Smith, 2017, UMI-tools: modelling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy, Genome Res., 27, 491, 10.1101/gr.209601.116 · DOI
Cited by
Sorted by citations
Controlled Interventions to Reduce Burnout in Physicians: A Systematic Review and Meta-analysis
Panagioti, M., Panagopoulou, E., Bower, P., Lewith, G., Kontopantelis, E., Chew-Graham, C., Dawson, S., van Marwijk, H., Geraghty, K., & Esmail, A. (2017) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2016.7674 · Cited by 1,395
Next-Generation Sequencing Technology: Current Trends and Advancements
Satam, H., Joshi, K., Mangrolia, U., Waghoo, S., Zaidi, G., Rawool, S., Thakare, R. P., Banday, S., Mishra, A. K., Das, G., et al. (2023) · Biology · doi:10.3390/biology12070997 · Cited by 1,299
Chen, R. E., Zhang, X., Case, J. B., Winkler, E. S., Liu, Y., VanBlargan, L. A., Liu, J., Errico, J. M., Xie, X., Suryadevara, N., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01294-w · Cited by 881
Prospective mapping of viral mutations that escape antibodies used to treat COVID-19
Starr, T. N., Greaney, A. J., Addetia, A., Hannon, W. W., Choudhary, M. C., Dingens, A. S., Li, J. Z., & Bloom, J. D. (2021) · Science · doi:10.1126/science.abf9302 · Cited by 743
Genome-wide programmable transcriptional memory by CRISPR-based epigenome editing
Nuñez, J. K., Chen, J., Pommier, G. C., Cogan, J. Z., Replogle, J. M., Adriaens, C., Ramadoss, G. N., Shi, Q., Hung, K. L., Samelson, A. J., et al. (2021) · Cell · doi:10.1016/j.cell.2021.03.025 · Cited by 760
Discovery and inhibition of an interspecies gut bacterial pathway for Levodopa metabolism
Maini Rekdal, V., Bess, E. N., Bisanz, J. E., Turnbaugh, P. J., & Balskus, E. P. (2019) · Science · doi:10.1126/science.aau6323 · Cited by 753
Loss of furin cleavage site attenuates SARS-CoV-2 pathogenesis
Johnson, B. A., Xie, X., Bailey, A. L., Kalveram, B., Lokugamage, K. G., Muruato, A., Zou, J., Zhang, X., Juelich, T., Smith, J. K., et al. (2021) · Nature · doi:10.1038/s41586-021-03237-4 · Cited by 694
Complete genomic and epigenetic maps of human centromeres
Altemose, N., Logsdon, G. A., Bzikadze, A. V., Sidhwani, P., Langley, S. A., Caldas, G. V., Hoyt, S. J., Uralsky, L., Ryabov, F. D., Shew, C. J., et al. (2022) · Science · doi:10.1126/science.abl4178 · Cited by 618
Federici, S., Kredo-Russo, S., Valdés-Mas, R., Kviatcovsky, D., Weinstock, E., Matiuhin, Y., Silberberg, Y., Atarashi, K., Furuichi, M., Oka, A., et al. (2022) · Cell · doi:10.1016/j.cell.2022.07.003 · Cited by 542
Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions
Narunsky-Haziza, L., Sepich-Poore, G. D., Livyatan, I., Asraf, O., Martino, C., Nejman, D., Gavert, N., Stajich, J. E., Amit, G., González, A., et al. (2022) · Cell · doi:10.1016/j.cell.2022.09.005 · Cited by 538
Shen, Z., Xiao, Y., Kang, L., Ma, W., Shi, L., Zhang, L., Zhou, Z., Yang, J., Zhong, J., Yang, D., et al. (2020) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa203 · Cited by 513
Case, J. B., Rothlauf, P. W., Chen, R. E., Liu, Z., Zhao, H., Kim, A. S., Bloyet, L.-M., Zeng, Q., Tahan, S., Droit, L., et al. (2020) · Cell Host & Microbe · doi:10.1016/j.chom.2020.06.021 · Cited by 410
Senescence atlas reveals an aged-like inflamed niche that blunts muscle regeneration
Moiseeva, V., Cisneros, A., Sica, V., Deryagin, O., Lai, Y., Jung, S., Andrés, E., An, J., Segalés, J., Ortet, L., et al. (2023) · Nature · doi:10.1038/s41586-022-05535-x · Cited by 423
Resurrection of endogenous retroviruses during aging reinforces senescence
Liu, X., Liu, Z., Wu, Z., Ren, J., Fan, Y., Sun, L., Cao, G., Niu, Y., Zhang, B., Ji, Q., et al. (2023) · Cell · doi:10.1016/j.cell.2022.12.017 · Cited by 390
Belk, J. A., Yao, W., Ly, N., Freitas, K. A., Chen, Y.-T., Shi, Q., Valencia, A. M., Shifrut, E., Kale, N., Yost, K. E., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.06.001 · Cited by 382
Self-citationHigh-Throughput Mapping of B Cell Receptor Sequences to Antigen Specificity
Setliff, I., Shiakolas, A. R., Pilewski, K. A., Murji, A. A., Mapengo, R. E., Janowska, K., Richardson, S., Oosthuysen, C., Raju, N., Ronsard, L., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.003 · Cited by 314
A non-canonical tricarboxylic acid cycle underlies cellular identity
Arnold, P. K., Jackson, B. T., Paras, K. I., Brunner, J. S., Hart, M. L., Newsom, O. J., Alibeckoff, S. P., Endress, J., Drill, E., Sullivan, L. B., et al. (2022) · Nature · doi:10.1038/s41586-022-04475-w · Cited by 304
Montassier, E., Valdés-Mas, R., Batard, E., Zmora, N., Dori-Bachash, M., Suez, J., & Elinav, E. (2021) · Nature Microbiology · doi:10.1038/s41564-021-00920-0 · Cited by 256
Transcriptome analysis reveals tumor microenvironment changes in glioblastoma
Hoogstrate, Y., Draaisma, K., Ghisai, S. A., van Hijfte, L., Barin, N., de Heer, I., Coppieters, W., van den Bosch, T. P. P., Bolleboom, A., Gao, Z., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.02.019 · Cited by 240
Truncated stathmin-2 is a marker of TDP-43 pathology in frontotemporal dementia
Prudencio, M., Humphrey, J., Pickles, S., Brown, A.-L., Hill, S. E., Kachergus, J. M., Shi, J., Heckman, M. G., Spiegel, M. R., Cook, C., et al. (2020) · Journal of Clinical Investigation · doi:10.1172/jci139741 · Cited by 231
Swanson, E., Lord, C., Reading, J., Heubeck, A. T., Genge, P. C., Thomson, Z., Weiss, M. D., Li, X.-J., Savage, A. K., Green, R. R., et al. (2021) · eLife · doi:10.7554/elife.63632 · Cited by 267
Bile Acids Are Substrates for Amine N-Acyl Transferase Activity by Bile Salt Hydrolase
Patterson, A., Rimal, B., Collins, S., Granda, M., Koo, I., Solanka, S., Hoque, N., Gentry, E., Yan, T., Bisanz, J., et al. (2022) · doi:10.21203/rs.3.rs-2050120/v1 · Cited by 252
Liu, Y., Lv, J., Liu, Z., Wang, J., Yang, B., Chen, W., Ou, L., Dai, X., Zhang, Z., & Zou, X. (2020) · Food Chemistry · doi:10.1016/j.foodchem.2019.125629 · Cited by 223
Xia, Y., Tang, W., Qian, X., Li, X., Cheng, F., Wang, K., Zhang, F., Zhang, C., Li, D., Song, J., et al. (2022) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2022-004656 · Cited by 228
Wang, B., Iriguchi, S., Waseda, M., Ueda, N., Ueda, T., Xu, H., Minagawa, A., Ishikawa, A., Yano, H., Ishi, T., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00730-z · Cited by 189
A TCF4-dependent gene regulatory network confers resistance to immunotherapy in melanoma
Pozniak, J., Pedri, D., Landeloos, E., Van Herck, Y., Antoranz, A., Vanwynsberghe, L., Nowosad, A., Roda, N., Makhzami, S., Bervoets, G., et al. (2024) · Cell · doi:10.1016/j.cell.2023.11.037 · Cited by 199
Peneder, P., Stütz, A. M., Surdez, D., Krumbholz, M., Semper, S., Chicard, M., Sheffield, N. C., Pierron, G., Lapouble, E., Tötzl, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23445-w · Cited by 200
Limeta, A., Ji, B., Levin, M., Gatto, F., & Nielsen, J. (2020) · JCI Insight · doi:10.1172/jci.insight.140940 · Cited by 185
Novel Fecal Biomarkers That Precede Clinical Diagnosis of Ulcerative Colitis
Galipeau, H. J., Caminero, A., Turpin, W., Bermudez-Brito, M., Santiago, A., Libertucci, J., Constante, M., Raygoza Garay, J. A., Rueda, G., Armstrong, S., et al. (2021) · Gastroenterology · doi:10.1053/j.gastro.2020.12.004 · Cited by 190
Loss of SATB1 Induces p21-Dependent Cellular Senescence in Post-mitotic Dopaminergic Neurons
Riessland, M., Kolisnyk, B., Kim, T. W., Cheng, J., Ni, J., Pearson, J. A., Park, E. J., Dam, K., Acehan, D., Ramos-Espiritu, L. S., et al. (2019) · Cell Stem Cell · doi:10.1016/j.stem.2019.08.013 · Cited by 180
BiT age: A transcriptome‐based aging clock near the theoretical limit of accuracy
Meyer, D. H., & Schumacher, B. (2021) · Aging Cell · doi:10.1111/acel.13320 · Cited by 165
Translational value of choroid plexus imaging for tracking neuroinflammation in mice and humans
Fleischer, V., Gonzalez-Escamilla, G., Ciolac, D., Albrecht, P., Küry, P., Gruchot, J., Dietrich, M., Hecker, C., Müntefering, T., Bock, S., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2025000118 · Cited by 152
Single-cell DNA methylation and 3D genome architecture in the human brain
Tian, W., Zhou, J., Bartlett, A., Zeng, Q., Liu, H., Castanon, R. G., Kenworthy, M., Altshul, J., Valadon, C., Aldridge, A., et al. (2023) · Science · doi:10.1126/science.adf5357 · Cited by 154
Spatial transcriptomic landscape unveils immunoglobin-associated senescence as a hallmark of aging
Ma, S., Ji, Z., Zhang, B., Geng, L., Cai, Y., Nie, C., Li, J., Zuo, Y., Sun, Y., Xu, G., et al. (2024) · Cell · doi:10.1016/j.cell.2024.10.019 · Cited by 162
Aging clocks based on accumulating stochastic variation
Meyer, D. H., & Schumacher, B. (2024) · Nature Aging · doi:10.1038/s43587-024-00619-x · Cited by 128
Hu, J., Kang, L., Li, Z., Feng, X., Liang, C., Wu, Z., Zhou, W., Liu, X., Yang, Y., & Chen, L. (2023) · Nature Communications · doi:10.1038/s41467-023-39432-2 · Cited by 114
Yu, L., Li, Z., Mei, H., Li, W., Chen, D., Liu, L., Zhang, Z., Sun, Y., Song, F., Chen, W., et al. (2021) · Clinical & Translational Immunology · doi:10.1002/cti2.1248 · Cited by 113
Lee, J. S., Goldstein, J. M., Moon, J. L., Herzegh, O., Bagarozzi, D. A., Oberste, M. S., Hughes, H., Bedi, K., Gerard, D., Cameron, B., et al. (2021) · PLOS ONE · doi:10.1371/journal.pone.0260487 · Cited by 110
Large-scale chromatin reorganization reactivates placenta-specific genes that drive cellular aging
Liu, Z., Ji, Q., Ren, J., Yan, P., Wu, Z., Wang, S., Sun, L., Wang, Z., Li, J., Sun, G., et al. (2022) · Developmental Cell · doi:10.1016/j.devcel.2022.05.004 · Cited by 109
Identification of trypsin-degrading commensals in the large intestine
Li, Y., Watanabe, E., Kawashima, Y., Plichta, D. R., Wang, Z., Ujike, M., Ang, Q. Y., Wu, R., Furuichi, M., Takeshita, K., et al. (2022) · Nature · doi:10.1038/s41586-022-05181-3 · Cited by 106
TCRdb: a comprehensive database for T-cell receptor sequences with powerful search function
Chen, S.-Y., Yue, T., Lei, Q., & Guo, A.-Y. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa796 · Cited by 106
Self-citationNovel Functional Sequences Uncovered through a Bovine Multiassembly Graph
Crysnanto, D., Leonard, A. S., Fang, Z.-H., & Pausch, H. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2101056118 · Cited by 96
Design of highly functional genome editors by modeling the universe of CRISPR-Cas sequences
Ruffolo, J. A., Nayfach, S., Gallagher, J., Bhatnagar, A., Beazer, J., Hussain, R., Russ, J., Yip, J., Hill, E., Pacesa, M., et al. (2024) · doi:10.1101/2024.04.22.590591 · Cited by 90
Fan, J., Lu, F., Qin, T., Peng, W., Zhuang, X., Li, Y., Hou, X., Fang, Z., Yang, Y., Guo, E., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01570-0 · Cited by 93
Bravo González-Blas, C., Matetovici, I., Hillen, H., Taskiran, I. I., Vandepoel, R., Christiaens, V., Sansores-García, L., Verboven, E., Hulselmans, G., Poovathingal, S., et al. (2024) · Nature Cell Biology · doi:10.1038/s41556-023-01316-4 · Cited by 85
Viroid-like colonists of human microbiomes
Zheludev, I. N., Edgar, R. C., Lopez-Galiano, M. J., de la Peña, M., Babaian, A., Bhatt, A. S., & Fire, A. Z. (2024) · Cell · doi:10.1016/j.cell.2024.09.033 · Cited by 73
Viroid-like colonists of human microbiomes
Zheludev, I. N., Edgar, R. C., Lopez-Galiano, M. J., de la Peña, M., Babaian, A., Bhatt, A. S., & Fire, A. Z. (2024) · doi:10.1101/2024.01.20.576352 · Cited by 73
Zhang, D., Zhang, J., Kalimuthu, S., Liu, J., Song, Z.-M., He, B.-B., Cai, P., Zhong, Z., Feng, C., Neelakantan, P., et al. (2023) · Microbiome · doi:10.1186/s40168-023-01540-y · Cited by 72
“Fastp 0.21.1 [61] with default parameters was adopted for detecting and removing low-quality sequencing reads.”
InfluentialMechanistic Basis for Maintenance of CHG DNA Methylation in Plants
Fang, J., Jiang, J., Leichter, S. M., Liu, J., Biswal, M., Khudaverdyan, N., Zhong, X., & Song, J. (2022) · Nature Communications · doi:10.1038/s41467-022-31627-3 · Cited by 69
A plate-based single-cell ATAC-seq workflow for fast and robust profiling of chromatin accessibility
Xu, W., Wen, Y., Liang, Y., Xu, Q., Wang, X., Jin, W., & Chen, X. (2021) · Nature Protocols · doi:10.1038/s41596-021-00583-5 · Cited by 67
Jang, K. K., Heaney, T., London, M., Ding, Y., Putzel, G., Yeung, F., Ercelen, D., Chen, Y.-H., Axelrad, J., Gurunathan, S., et al. (2023) · Cell Host & Microbe · doi:10.1016/j.chom.2023.08.002 · Cited by 70
Self-citationProteotoxic stress response drives T cell exhaustion and immune evasion
Wang, Y., Ma, A., Song, N.-J., Shannon, A. E., Amankwah, Y. S., Chen, X., Wu, W., Wang, Z., Saadey, A. A., Yousif, A., et al. (2025) · Nature · doi:10.1038/s41586-025-09539-1 · Cited by 68
The complete telomere-to-telomere sequence of a mouse genome
Liu, J., Li, Q., Hu, Y., Yu, Y., Zheng, K., Li, D., Qin, L., & Yu, X. (2024) · Science · doi:10.1126/science.adq8191 · Cited by 57
Strong pathogen competition in neonatal gut colonisation
Mäklin, T., Thorpe, H. A., Pöntinen, A. K., Gladstone, R. A., Shao, Y., Pesonen, M., McNally, A., Johnsen, P. J., Samuelsen, Ø., Lawley, T. D., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-35178-5 · Cited by 54
Fukushima, K., & Pollock, D. D. (2023) · Nature Ecology & Evolution · doi:10.1038/s41559-022-01932-7 · Cited by 55
Revealing the microbial heritage of traditional Brazilian cheeses through metagenomics
Kothe, C. I., Mohellibi, N., & Renault, P. (2022) · Food Research International · doi:10.1016/j.foodres.2022.111265 · Cited by 51
Dai, X., Bian, P., Hu, D., Luo, F., Huang, Y., Jiao, S., Wang, X., Gong, M., Li, R., Cai, Y., et al. (2023) · Genome Research · doi:10.1101/gr.277481.122 · Cited by 43
Computational methods and challenges in analyzing intratumoral microbiome data
Wang, Q., Liu, Z., Ma, A., Li, Z., Liu, B., & Ma, Q. (2023) · Trends in Microbiology · doi:10.1016/j.tim.2023.01.011 · Cited by 37
Koumaki, K., Kontogianni, G., Kosmidou, V., Pahitsa, F., Kritsi, E., Zervou, M., Chatziioannou, A., Souliotis, V. L., Papadodima, O., & Pintzas, A. (2021) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2020.166061 · Cited by 36
Eldesouky, H. E., Lanman, N. A., Hazbun, T. R., & Seleem, M. N. (2020) · Virulence · doi:10.1080/21505594.2020.1838741 · Cited by 37
Kiu, R., Darby, E. M., Alcon-Giner, C., Acuna-Gonzalez, A., Camargo, A., Lamberte, L. E., Phillips, S., Sim, K., Shaw, A. G., Clarke, P., et al. (2025) · Nature communications · doi:10.1038/s41467-025-62584-2 · Cited by 36
Oxygen metabolism in descendants of the archaeal-eukaryotic ancestor
Appler, K. E., Lingford, J. P., Gong, X., Panagiotou, K., Leão, P., Langwig, M. V., Greening, C., Ettema, T. J. G., De Anda, V., & Baker, B. J. (2026) · Nature · doi:10.1038/s41586-026-10128-z · Cited by 36
Oxygen metabolism in descendants of the archaeal-eukaryotic ancestor
Appler, K. E., Lingford, J. P., Gong, X., Panagiotou, K., Leão, P., Langwig, M., Greening, C., Ettema, T. J. G., De Anda, V., & Baker, B. J. (2024) · doi:10.1101/2024.07.04.601786 · Cited by 36
Zhang, Y., Tang, C., He, Y., Zhang, Y., Li, Q., Zhang, T., Zhao, B., Tong, A., Zhong, Q., & Zhong, Z. (2024) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2024.117540 · Cited by 36
Narayanan, A., Kumar, P., Chauhan, A., Kumar, M., Yadav, K., Banerjee, A., Sharma, R. D., Rudramurthy, S. M., Chakrabarti, A., Sanyal, K., et al. (2022) · mBio · doi:10.1128/mbio.03052-22 · Cited by 35
Unravelling reference bias in ancient DNA datasets
Dolenz, S., van der Valk, T., Jin, C., Oppenheimer, J., Sharif, M. B., Orlando, L., Shapiro, B., Dalén, L., & Heintzman, P. D. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae436 · Cited by 33
“23.2, and removed reads below a length of 20 bp (or 30 bp in the cases of American mastodon and Siberian unicorn) and those that could not be merged (Chen et al. 2018).”
BackgroundInfluentialWang, J., Jiang, M., Hu, Y., Lei, Y., Zhu, Y., Xiong, H., & He, C. (2023) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2023.114769 · Cited by 33
Breastfeeding and early Bifidobacterium-driven microbial colonization shape the infant gut resistome
Samarra, A., Alcañiz, A. J., Martínez-Costa, C., Marina, A., Comas, I., Segata, N., Quijada, N. M., & Collado, M. C. (2025) · Nature Communications · doi:10.1038/s41467-025-61154-w · Cited by 32
Jaric, I., Voelkl, B., Clerc, M., Schmid, M. W., Novak, J., Rosso, M., Rufener, R., von Kortzfleisch, V. T., Richter, S. H., Buettner, M., et al. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001837 · Cited by 32
Association of SPI1 haplotypes with altered SPI1 gene expression and Alzheimer’s disease risk
Cao, H., Initiative, A. D. N., Zhou, X., Chen, Y., Ip, F. C. F., Chen, Y., Lai, N. C. H., Lo, R. M. N., Tong, E. P. S., Mok, V. C. T., et al. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-215311 · Cited by 28
“subjected the raw reads to fastp for quality con145 trol, reads trimming, and filtration [11] and aligned 146”
Self-citationFu, Y., Lin, Y., Deng, Z., Chen, M., Yu, G., Jiang, P., Zhang, X., Liu, J., & Yang, X. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.134590 · Cited by 27
Shangguan, S., Ehrenberg, P. K., Geretz, A., Yum, L., Kundu, G., May, K., Fourati, S., Nganou-Makamdop, K., Williams, L. D., Sawant, S., et al. (2021) · eLife · doi:10.7554/elife.69577 · Cited by 26
Jia, R., Zhang, R., Gangurde, S. S., Tang, C., Jiang, B., Li, G., & Wang, Z. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.993682 · Cited by 20
Garrigues, S., Peng, M., Kun, R. S., & de Vries, R. P. (2023) · mBio · doi:10.1128/mbio.00668-23 · Cited by 22
Polymerization of ZBTB transcription factors regulates chromatin occupancy
Park, P. M. C., Park, J., Brown, J., Hunkeler, M., Roy Burman, S. S., Donovan, K. A., Yoon, H., Nowak, R. P., Słabicki, M., Ebert, B. L., et al. (2024) · Molecular Cell · doi:10.1016/j.molcel.2024.06.010 · Cited by 22
Kaulich, E., Waselenchuk, Q., Fürst, N., Desch, K., Mosbacher, J., Ciirdaeva, E., Juengling, M., Tushev, G., Langer, J., & Schuman, E. M. (2024) · doi:10.1101/2024.08.05.606570 · Cited by 22
Ceballos-Garzon, A., Monteoliva, L., Gil, C., Alvarez-Moreno, C., Vega-Vela, N. E., Engelthaler, D. M., Bowers, J., Le Pape, P., & Parra-Giraldo, C. M. (2022) · Journal of Antimicrobial Chemotherapy · doi:10.1093/jac/dkab454 · Cited by 18
Lhx2 specifically expressed in HSCs promotes liver regeneration and inhibits liver fibrosis
Tao, J., Wu, Z., Liang, Y., Wang, J., Tang, M., Huang, S., Jiang, F., Zhou, G., Guo, L., Yuan, S., et al. (2025) · Hepatology · doi:10.1097/hep.0000000000001201 · Cited by 19
Lyu, T., Liu, Y., Li, B., Xu, R., Guo, J., & Zhu, D. (2023) · Biology Direct · doi:10.1186/s13062-023-00390-w · Cited by 18
Chia, M., Ng, A. H. Q., Ravikrishnan, A., Mohamed Naim, A. N., Wearne, S., Common, J., & Nagarajan, N. (2026) · Nature Biotechnology · doi:10.1038/s41587-025-02797-4 · Cited by 18
Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet
Viner, C., Ishak, C. A., Johnson, J., Walker, N. J., Shi, H., Sjöberg-Herrera, M. K., Shen, S. Y., Lardo, S. M., Adams, D. J., Ferguson-Smith, A. C., et al. (2016) · doi:10.1101/043794 · Cited by 18
Comparative Genomics-Based Probiotic Relevance of Limosilactobacillus fermentum KUB-D18
Phujumpa, P., Muangham, S., Jatuponwiphat, T., Koffas, M., Nakphaichit, M., & Vongsangnak, W. (2022) · Gene · doi:10.1016/j.gene.2022.146747 · Cited by 18
Ancient DNA Confirms Diverse Origins of Early Post-Columbian Cattle in the Americas
Delsol, N., Stucky, B. J., Oswald, J. A., Cobb, C. R., Emery, K. F., & Guralnick, R. (2023) · Scientific Reports · doi:10.1038/s41598-023-39518-3 · Cited by 17
Salama, E. A., Elgammal, Y., Wijeratne, A., Lanman, N. A., Utturkar, S. M., Farhangian, A., Li, J., Meunier, B., Hazbun, T. R., & Seleem, M. N. (2024) · Emerging Microbes & Infections · doi:10.1080/22221751.2024.2322649 · Cited by 16
Cazin, C., Boumerdassi, Y., Martinez, G., Fourati Ben Mustapha, S., Whitfield, M., Coutton, C., Thierry-Mieg, N., Di Pizio, P., Rives, N., Arnoult, C., et al. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22042187 · Cited by 16
Gut microbiota drives structural variation of exogenous probiotics to enhance colonization
Jiang, S., Huang, S., Zhang, Z., Ma, W., Han, Z., Song, Y., Huo, D., Cui, W., & Zhang, J. (2025) · Gut Microbes · doi:10.1080/19490976.2025.2503371 · Cited by 15
“46”
Terlouw, D., Boot, A., Ducarmon, Q. R., Nooij, S., Suerink, M., van Leerdam, M. E., van Egmond, D., Tops, C. M., Zwittink, R. D., Ruano, D., et al. (2024) · BMC Cancer · doi:10.1186/s12885-024-11849-y · Cited by 15
Milia, S., Leonard, A. S., Mapel, X. M., Bernal Ulloa, S. M., Drögemüller, C., & Pausch, H. (2025) · Genome Research · doi:10.1101/gr.279064.124 · Cited by 22
High performance of a GPU-accelerated variant calling tool in genome data analysis
Zhang, Q., Liu, H., & Bu, F. (2021) · doi:10.1101/2021.12.12.472266 · Cited by 13
Zhang, S., Li, P., Zhang, X., Ding, Y., Wang, T., Lee, S., Xu, Y., Lim, C., & Shang, N. (2024) · Nutrients · doi:10.3390/nu16173014 · Cited by 13
Lin, Y., Li, Y., Zhu, H., Tang, L., & Xu, J. (2025) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1511602 · Cited by 13
ChatGPT and its application in the field of mental health.
Bhattacharyya, R., Chakraborty, K., & Neogi, R. (2023) · Journal of SAARC Psychiatric Federation · doi:10.4103/jspf.jspf_9_23 · Cited by 12
High-throughput Selection of Human de novo -emerged sORFs with High Folding Potential
Aubel, M., Buchel, F., Heames, B., Jones, A., Honc, O., Bornberg-Bauer, E., & Hlouchova, K. (2024) · Genome Biology and Evolution · doi:10.1093/gbe/evae069 · Cited by 12
Guo, W., Gou, X., Yu, L., Zhang, Q., Yang, P., Pang, M., Pang, X., Pang, C., Wei, Y., & Zhang, X. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1129470 · Cited by 10
Mapping of Pituitary Stress-Induced Gene Regulation Connects Nrcam to Negative Emotions
Olsen, M. B., Sannes, A.-C., Yang, K., Nielsen, M. B., Einarsen, S. V., Christensen, J. O., Pallesen, S., Bjørås, M., & Gjerstad, J. (2022) · iScience · doi:10.1016/j.isci.2022.104953 · Cited by 9
Antifungal activity of the repurposed drug disulfiram against Cryptococcus neoformans
Peng, M., Zhang, C., Duan, Y.-Y., Liu, H.-B., Peng, X.-Y., Wei, Q., Chen, Q.-Y., Sang, H., & Kong, Q.-T. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1268649 · Cited by 9
Tracing unknown tumor origins with a biological-pathway-based transformer model
Xie, J., Chen, Y., Luo, S., Yang, W., Lin, Y., Wang, L., Ding, X., Tong, M., & Yu, R. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100797 · Cited by 9
Self-citationLi, Y., Hou, X., Li, R., Liao, K., Ma, L., Wang, X., Ji, P., Kong, H., Xia, Y., Ding, H., et al. (2023) · BMC Microbiology · doi:10.1186/s12866-023-03105-3 · Cited by 7
Mitogenome Diversity and Evolution of Bos indicus Cattle in India
Sarath Kumar, T., Singh, S., Ganguly, I., & Dixit, S. P. (2025) · Gene · doi:10.1016/j.gene.2024.149096 · Cited by 7
Drug screening at single-organoid resolution via bioprinting and interferometry
Tebon, P. J., Wang, B., Markowitz, A. L., Davarifar, A., Krawczuk, P., Murray, G., Nguyen, H. T. L., Tavanaie, N., Nguyen, T. L., Boutros, P. C., et al. (2021) · doi:10.1101/2021.10.03.462896 · Cited by 155
Metadata
- Authors
- Chen, S., Zhou, Y., Chen, Y., & Gu, J.
- Affiliations
- Chinese Academy of Sciences (CN)
- Department of Bioinformatics, HaploX Biotechnology, Shenzhen, China
- Corresponding author
- Chen, Shifu
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-09-01
- Volume
- 34
- Issue
- 17
- Pages
- i884-i890
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC6129281
- OpenAlex
- W2951912016
- Cited by
- 29,721
- References
- 6
- Citations by year
- 2026: 6556 · 2025: 6937 · 2024: 6063 · 2023: 4192 · 2022: 3147 · 2021: 1820 · 2020: 787 · 2019: 187 · 2018: 9
- FWCI
- 258.91
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Special Funds for Future Industries of Shenzhen (JSGG20160229123927512)
- National Science Foundation of China (61472411)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Mathematics › Theoretical Computer Science › Algorithms and Data Compression
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- quality control, adapter trimming, quality filtering, multithreading, C++, high-throughput sequencing data, fastp
- MeSH terms
- Humans, Programming Languages, Quality Control
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