Opportunities and challenges in long-read sequencing data analysis

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

<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

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

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

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

Pilon: An Integrated Tool for Comprehensive Microbial Variant Detection and Genome Assembly Improvement

Walker, B. J., Abeel, T., Shea, T., Priest, M., Abouelliel, A., Sakthikumar, S., Cuomo, C. A., Zeng, Q., Wortman, J. R., Young, S., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112963 · Cited by 9,130

Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads

Wick, R. R., Judd, L. M., Gorrie, C. L., & Holt, K. E. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005595 · Cited by 8,728

Canu: scalable and accurate long-read assembly via adaptive k -mer weighting and repeat separationPreprint

Koren, S., Walenz, B. P., Berlin, K., Miller, J. R., Bergman, N. H., & Phillippy, A. M. (2017) · Genome Research · doi:10.1101/gr.215087.116 · Cited by 7,260

Assembly of long, error-prone reads using repeat graphs

Kolmogorov, M., Yuan, J., Lin, Y., & Pevzner, P. A. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0072-8 · Cited by 5,986

Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

McCarthy, D. J., Chen, Y., & Smyth, G. K. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks042 · Cited by 5,195

Adversarial domain translation networks for integrating large-scale atlas-level single-cell datasets

Zhao, J., Wang, G., Ming, J., Lin, Z., Wang, Y., Agarwal, S., Agrawal, A., Al‐Moujahed, A., Alam, A. A., Albertelli, M. A., et al. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00251-y · Cited by 43

Seminal and vagino-uterine microbiome and their individual and interactive effects on cattle fertility

Luecke, S. M., Webb, E. M., Dahlen, C. R., Reynolds, L. P., & Amat, S. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.1029128 · Cited by 42

Oarfish: enhanced probabilistic modeling leads to improved accuracy in long read transcriptome quantification

Jousheghani, Z. Z., Singh, N., & Patro, R. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf240 · Cited by 48

A review of the resistome within the digestive tract of livestock

Ma, T., McAllister, T. A., & Guan, L. L. (2021) · Journal of Animal Science and Biotechnology/Journal of animal science and biotechnology · doi:10.1186/s40104-021-00643-6 · Cited by 51

maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks

Cazares, T. A., Rizvi, F. W., Iyer, B., Chen, X., Kotliar, M., Bejjani, A. T., Wayman, J. A., Donmez, O. A., Wronowski, B., Parameswaran, S., et al. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010863 · Cited by 44

TFvelo: gene regulation inspired RNA velocity estimation

Li, J., Pan, X., Yuan, Y., & Shen, H. (2024) · Nature Communications · doi:10.1038/s41467-024-45661-w · Cited by 47

Wnt/β-catenin signalling is required for pole-specific chromatin remodeling during planarian regeneration

Pascual-Carreras, E., Marin-Barba, M., Castillo-Lara, S., Coronel-Córdoba, P., Magri, M. S., Wheeler, G. N., Gómez-Skármeta, J. L., Abril, J. F., Saló, E., & Adell, T. (2023) · Nature Communications · doi:10.1038/s41467-023-35937-y · Cited by 46

A roadmap to establish a comprehensive platform for sustainable manufacturing of natural products in yeast

Naseri, G. (2023) · Nature Communications · doi:10.1038/s41467-023-37627-1 · Cited by 46

Isoform-resolved transcriptome of the human preimplantation embryo

Torre, D., Francoeur, N. J., Kalma, Y., Carmel, I. G., Melo, B. S., Deikus, G. Y., Allette, K., Flohr, R., Fridrikh, M., Vlachos, K., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42558-y · Cited by 43

Computing the Role of Alternative Splicing in Cancer

Liu, Z., & Rabadán, R. (2021) · Trends in cancer · doi:10.1016/j.trecan.2020.12.015 · Cited by 43

Recovery of complete genomes and non-chromosomal replicons from activated sludge enrichment microbial communities with long read metagenome sequencing

Arumugam, K., Bessarab, I. N., Haryono, M., Liu, X., Zuniga-Montanez, R. E., Roy, S., Qiu, G., Drautz‐Moses, D. I., Law, Y., Wuertz, S., et al. (2021) · npj Biofilms and Microbiomes · doi:10.1038/s41522-021-00196-6 · Cited by 43

Opportunities and challenges for high‐quality biodiversity tissue archives in the age of long‐read sequencing

Blom, M. P. K. (2021) · Molecular Ecology · doi:10.1111/mec.15909 · Cited by 47

Application of long-read sequencing to the detection of structural variants in human cancer genomes

Sakamoto, Y., Zaha, S., Suzuki, Y., Seki, M., & Suzuki, A. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.07.030 · Cited by 41

The intronic branch point sequence is under strong evolutionary constraint in the bovine and human genome

Kadri, N. K., Mapel, X. M., & Pausch, H. (2021) · Communications Biology · doi:10.1038/s42003-021-02725-7 · Cited by 40

Haplotype resolved chromosome level genome assembly of Citrus australis reveals disease resistance and other citrus specific genes

Nakandala, U., Masouleh, A. K., Smith, M. W., Furtado, A., Mason, P. J., Constantin, L., & Henry, R. J. (2023) · Horticulture Research · doi:10.1093/hr/uhad058 · Cited by 40

Epigenetic biomarkers for disease susceptibility and preventative medicine

Skinner, M. K. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2023.11.015 · Cited by 45

Long-read sequencing of an advanced cancer cohort resolves rearrangements, unravels haplotypes, and reveals methylation landscapes

O’Neill, K., Pleasance, E., Fan, J., Akbari, V., Chang, G., Dixon, K., Csizmók, V., MacLennan, S., Porter, V., Galbraith, A., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100674 · Cited by 47

CEDAR, an online resource for the reporting and exploration of complexome profiling data

van Strien, J., Haupt, A., Schulte, U., Braun, H., Cabrera‐Orefice, A., Choudhary, J. S., Evers, F., Fernández‐Vizarra, E., Guerrero‐Castillo, S., Kooij, T. W. A., et al. (2021) · Biochimica et Biophysica Acta (BBA) - Bioenergetics · doi:10.1016/j.bbabio.2021.148411 · Cited by 42

Machine learning for single-cell genomics data analysis

Raimundo, F., Meng-Papaxanthos, L., Vallot, C., & Vert, J. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.04.006 · Cited by 46

Benchmarking taxonomic classifiers with Illumina and Nanopore sequence data for clinical metagenomic diagnostic applications

Govender, K. N., & Eyre, D. W. (2022) · Microbial Genomics · doi:10.1099/mgen.0.000886 · Cited by 48

Accelerated nanopore basecalling with SLOW5 data format

Samarakoon, H., Ferguson, J. M., Gamaarachchi, H., & Deveson, I. W. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad352 · Cited by 43

Pangenomes as a Resource to Accelerate Breeding of Under-Utilised Crop Species

Fernandez, C. G. T., Nestor, B. J., Danilevicz, M. F., Gill, M., Petereit, J., Bayer, P. E., Finnegan, P. M., Batley, J., & Edwards, D. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23052671 · Cited by 43

An outlook on the current challenges and opportunities in DNA data storage

Raza, M. H., Desai, S. S., Aravamudhan, S., & Zadegan, R. M. (2023) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108155 · Cited by 42

Splicing defects in rare diseases: transcriptomics and machine learning strategies towards genetic diagnosis

Wang, R., Helbig, I., Edmondson, A. C., Lin, L., & Xing, Y. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad284 · Cited by 42

Fulgor: a fast and compact k-mer index for large-scale matching and color queries

Fan, J., Singh, N., Khan, J., Pibiri, G. E., & Patro, R. (2024) · Algorithms for Molecular Biology · doi:10.1186/s13015-024-00251-9 · Cited by 52

The role of pangenomics in orphan crop improvement

Hu, H., Zhao, J., Thomas, W. J. W., Batley, J., & Edwards, D. (2025) · Nature Communications · doi:10.1038/s41467-024-55260-4 · Cited by 52

Rumen metaproteomics: Closer to linking rumen microbial function to animal productivity traits

Andersen, T. O., Kunath, B. J., Hagen, L. H., Arntzen, M. Ø., & Pope, P. B. (2020) · Methods · doi:10.1016/j.ymeth.2020.07.011 · Cited by 42

Detecting and phasing minor single-nucleotide variants from long-read sequencing data

Feng, Z., Clemente, J. C., Wong, B. Y., & Schadt, E. E. (2021) · Nature Communications · doi:10.1038/s41467-021-23289-4 · Cited by 43

Comparison of SARS-CoV-2 sequencing using the ONT GridION and the Illumina MiSeq

Tshiabuila, D., Giandhari, J., Pillay, S., Ramphal, U., Ramphal, Y., Maharaj, A., Anyaneji, U. J., Naidoo, Y., Tegally, H., San, E. J., et al. (2022) · BMC Genomics · doi:10.1186/s12864-022-08541-5 · Cited by 43

Benchmarking of long-read sequencing, assemblers and polishers for yeast genome

Zhang, X., Liu, C., Yang, S., Wang, X., Bai, F., & Wang, Z. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac146 · Cited by 43

High-Quality Genome Assembly of Fusarium oxysporum f. sp. lini

Krasnov, G. S., Pushkova, E. N., Novakovskiy, R. O., Kudryavtseva, L. P., Rozhmina, T. A., Dvorianinova, E. M., Povkhova, L. V., Kudryavtseva, A. V., Dmitriev, A. A., & Melnikova, N. V. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00959 · Cited by 40

Application of second-generation sequencing (SGS) and third generation sequencing (TGS) in aquaculture breeding program

Wong, L. L., Razali, S. A., Deris, Z. M., Danish‐Daniel, M., Tan, M. P., Nor, S. A. M., Ma, H., Min, W., Yantao, L., Asaduzzaman, M., et al. (2021) · Aquaculture · doi:10.1016/j.aquaculture.2021.737633 · Cited by 40

Chromosome-Level Reference Genomes for Two Strains of Caenorhabditis briggsae : An Improved Platform for Comparative Genomics

Stevens, L., Moya, N. D., Tanny, R. E., Gibson, S. B., Tracey, A., Na, H., Zhan, Y., Chitrakar, R., Dekker, J., Walhout, A. J. M., et al. (2022) · Genome Biology and Evolution · doi:10.1093/gbe/evac042 · Cited by 40

Spatial epitranscriptomics reveals A-to-I editome specific to cancer stem cell microniches

Lee, A. C., Lee, Y., Choi, A., Lee, H., Shin, K., Lee, H., Kim, J. Y., Ryu, H. S., Kim, H. S., Ryu, S. Y., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30299-3 · Cited by 44

A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome

Zhang, Z., Feng, F., Qiu, Y., & Liu, J. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad436 · Cited by 40

Genome annotation: from human genetics to biodiversity genomics

Guigó, R. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100375 · Cited by 49

Intraspecific microdiversity and ecological drivers of lactic acid bacteria in naturally fermented milk ecosystem

You, L., Jin, H., Kwok, L., Lv, R., Zhao, Z., Bilige, M., Sun, Z., Liu, W., & Zhang, H. (2023) · 中国科学通报：英文版 · doi:10.1016/j.scib.2023.09.001 · Cited by 43

Novel Tri-Segmented Rhabdoviruses: A Data Mining Expedition Unveils the Cryptic Diversity of Cytorhabdoviruses

Bejerman, N., Dietzgen, R. G., & Debat, H. (2023) · Viruses · doi:10.3390/v15122402 · Cited by 40

Lessons learned: next-generation sequencing applied to undiagnosed genetic diseases

Schuler, B. A., Nelson, E. T., Koziura, M. E., Cogan, J. D., Hamid, R., & Phillips, J. A. (2022) · Journal of Clinical Investigation · doi:10.1172/jci154942 · Cited by 41

Long-Read Sequencing Improves the Detection of Structural Variations Impacting Complex Non-Coding Elements of the Genome

Begum, G., Albanna, A., Bankapur, A., Nassir, N., Tambi, R., Berdiev, B. K., Akter, H., Karuvantevida, N. S., Kellam, B., Alhashmi, D., et al. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22042060 · Cited by 39

Identifying common transcriptome signatures of cancer by interpreting deep learning models

Jha, A., Quesnel-Vallières, M., Wang, D., Thomas-Tikhonenko, A., Lynch, K. W., & Barash, Y. (2022) · Genome Biology · doi:10.1186/s13059-022-02681-3 · Cited by 39

Population‐specific responses to developmental temperature in the arboviral vector Aedes albopictus: Implications for climate change

Carlassara, M., Khorramnejad, A., Oker, H., Bahrami, R., Lozada‐Chávez, A. N., Mancini, M. V., Quaranta, S., Body, M. J. A., Lahondère, C., & Bonizzoni, M. (2024) · Global Change Biology · doi:10.1111/gcb.17226 · Cited by 38

The additional diagnostic yield of long-read sequencing in undiagnosed rare diseasesPreprint

Gobbo, G. F. D., & Boycott, K. M. (2025) · Genome Research · doi:10.1101/gr.279970.124 · Cited by 44

Comparative Analysis of PacBio and Oxford Nanopore Sequencing Technologies for Transcriptomic Landscape Identification of Penaeus monodon

Udaondo, Z., Sittikankaew, K., Uengwetwanit, T., Wongsurawat, T., Sonthirod, C., Jenjaroenpun, P., Pootakham, W., Karoonuthaisiri, N., & Nookaew, I. (2021) · Life · doi:10.3390/life11080862 · Cited by 38

A robust and scalable graph neural network for accurate single-cell classification

Zeng, Y., Wei, Z., Pan, Z., Lu, Y., & Yang, Y. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab570 · Cited by 42

Integrating taxonomic signals from MAGs and contigs improves read annotation and taxonomic profiling of metagenomes

Hauptfeld, E., Pappas, N., van Iwaarden, S., Snoek, B. L., Aldas-Vargas, A., Dutilh, B. E., & von Meijenfeldt, F. A. B. (2024) · Nature Communications · doi:10.1038/s41467-024-47155-1 · Cited by 38

Maternal gestational diabetes mellitus associates with altered gut microbiome composition and head circumference abnormalities in male offspring

Wang, S., Liu, Y., Tam, W. H., Ching, J. Y. L., Xu, W., Yan, S., Qin, B., Lin, L., Peng, Y., Zhu, J., et al. (2024) · Cell Host & Microbe · doi:10.1016/j.chom.2024.06.005 · Cited by 43

Integrative genomics reveals the polygenic basis of seedlessness in grapevine

Wang, X., Liu, Z., Zhang, F., Xiao, H., Cao, S., Xue, H., Liú, W., Su, Y., Liu, Z., Zhong, H., et al. (2024) · Current Biology · doi:10.1016/j.cub.2024.07.022 · Cited by 38

Mimicking and analyzing the tumor microenvironment

Crouigneau, R., Li, Y., Auxillos, J., Gonçalves‐Alves, E., Marie, R., Sandelin, A., & Pedersen, S. F. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100866 · Cited by 45

Microbes and pathogens associated with shrimps - implications and review of possible control strategies

Bhassu, S., Shama, M., Tiruvayipati, S., Soo, T. C. C., Ahmed, N., & Yusoff, K. (2024) · Frontiers in Marine Science · doi:10.3389/fmars.2024.1397708 · Cited by 38

FINDER: an automated software package to annotate eukaryotic genes from RNA-Seq data and associated protein sequences

Banerjee, S., Bhandary, P., Woodhouse, M., Sen, T. Z., Wise, R. P., & Andorf, C. M. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04120-9 · Cited by 35

SeGraM

Cali, D. S., Kanellopoulos, K., Lindegger, J., Bingöl, Z., Kalsi, G. S., Zuo, Z., Fırtına, C., Cavlak, M. B., Kim, J. S., Ghiasi, N. M., et al. (2022) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3470496.3527436 · Cited by 56

LPA disruption with AAV-CRISPR potently lowers plasma apo(a) in transgenic mouse model: A proof-of-concept study

Doerfler, A. M., Park, S. H., Assini, J. M., Youssef, A., Saxena, L., Yaseen, A. B., Giorgi, M. D., Chuecos, M. A., Hurley, A. E., Li, A., et al. (2022) · Molecular Therapy — Methods & Clinical Development · doi:10.1016/j.omtm.2022.10.009 · Cited by 37

Species-specific basecallers improve actual accuracy of nanopore sequencing in plants

Ferguson, S., McLay, T. G. B., Andrew, R. L., Bruhl, J. J., Schwessinger, B., Borevitz, J., & Jones, A. (2022) · Plant Methods · doi:10.1186/s13007-022-00971-2 · Cited by 42

homologizer: Phylogenetic phasing of gene copies into polyploid subgenomes

Freyman, W. A., Johnson, M. G., & Rothfels, C. J. (2023) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14072 · Cited by 36

Recovering Escherichia coli Plasmids in the Absence of Long-Read Sequencing Data

Paganini, J., Plantinga, N. L., Arredondo-Alonso, S., Willems, R. J. L., & Schürch, A. C. (2021) · Microorganisms · doi:10.3390/microorganisms9081613 · Cited by 37

Non-coding Natural Antisense Transcripts: Analysis and Application

Krappinger, J. C., Bonstingl, L., Pansy, K., Sallinger, K., Wreglesworth, N. I., Grinninger, L., Deutsch, A. J. A., Heliebi, A. E., Kroneis, T., McFarlane, R. J., et al. (2021) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2021.08.005 · Cited by 38

hapCon: estimating contamination of ancient genomes by copying from reference haplotypes

Huang, Y., & Ringbauer, H. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac390 · Cited by 39

SPUMONI 2: improved classification using a pangenome index of minimizer digests

Ahmed, O., Rossi, M., Gagie, T., Boucher, C. A., & Langmead, B. (2023) · Genome biology · doi:10.1186/s13059-023-02958-1 · Cited by 38

Strategies for Natural Product Discovery by Unlocking Cryptic Biosynthetic Gene Clusters in Fungi

Hur, J. Y., Jeong, E., Kim, Y. C., & Lee, S. R. (2023) · Separations · doi:10.3390/separations10060333 · Cited by 36

Environmentally robust cis -regulatory changes underlie rapid climatic adaptation

Ballinger, M. A., Mack, K. L., Durkin, S. M., Riddell, E. A., & Nachman, M. W. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2214614120 · Cited by 38

Genetics of Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome: advancements and implications

Herlin, M. K. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1368990 · Cited by 55

Strainy: phasing and assembly of strain haplotypes from long-read metagenome sequencing

Kazantseva, E., Donmez, A., Frolova, M. S., Pop, M., & Kolmogorov, M. (2024) · Nature Methods · doi:10.1038/s41592-024-02424-1 · Cited by 38

Seed-and-vote based in-memory accelerator for DNA read mapping

Laguna, A. F., Gamaarachchi, H., Yin, X., Niemier, M. T., Parameswaran, S., & Hu, X. S. (2020) · doi:10.1145/3400302.3415651 · Cited by 39

Potential of Whole-Genome Sequencing-Based Pharmacogenetic Profiling

Caspar, S. M., Schneider, T., Stoll, P., Meienberg, J., & Mátyás, G. (2021) · Pharmacogenomics · doi:10.2217/pgs-2020-0155 · Cited by 37

A chromosome‐level Populus qiongdaoensis genome assembly provides insights into tropical adaptation and a cryptic turnover of sex determination

Li, Y., Wang, D., Wang, W., Yang, W., Gao, J., Zhang, W., Shan, L., Kang, M., Chen, Y., & Ma, T. (2022) · Molecular Ecology · doi:10.1111/mec.16566 · Cited by 37

Quantifying the role of transcript levels in mediating DNA methylation effects on complex traits and diseases

Sadler, M. C., Auwerx, C., Lepik, K., Porcu, E., & Kutalik, Z. (2022) · Nature Communications · doi:10.1038/s41467-022-35196-3 · Cited by 35

Expression of Human Endogenous Retrovirus Group K (HERV-K) HML-2 Correlates with Immune Activation of Macrophages and Type I Interferon Response

Russ, E. U., Mikhalkevich, N., & Iordanskiy, S. (2023) · Microbiology Spectrum · doi:10.1128/spectrum.04438-22 · Cited by 43

Pangenome Graph Construction from Genome Alignment with Minigraph-Cactus

Hickey, G., Monlong, J., Ebler, J., Novak, A., Eizenga, J. M., Gao, Y., Human Pangenome Reference Consortium, Marschall, T., Li, H., & Paten, B. (2022) · doi:10.1101/2022.10.06.511217 · Cited by 345

High molecular weight DNA extraction methods lead to high quality filamentous ascomycete fungal genome assemblies using Oxford Nanopore sequencing

Petersen, C., Sørensen, T., Westphal, K. R., Fechete, L. I., Søndergaard, T. E., Sørensen, J. L., & Nielsen, K. L. (2022) · Microbial Genomics · doi:10.1099/mgen.0.000816 · Cited by 36

From genome structure to function: insights into structural variation in microbiology

West, P. T., Chanin, R. B., & Bhatt, A. S. (2022) · Current Opinion in Microbiology · doi:10.1016/j.mib.2022.102192 · Cited by 36

Amplicon-Based Microbiome Profiling: From Second- to Third-Generation Sequencing for Higher Taxonomic Resolution

Notario, E., Visci, G., Fosso, B., Gissi, C., Tanasković, N., Rescigno, M., Marzano, M., & Pesole, G. (2023) · Genes · doi:10.3390/genes14081567 · Cited by 38

TEQUILA-seq: a versatile and low-cost method for targeted long-read RNA sequencing

Wang, F., Xu, Y., Wang, R., Zhang, B. D., Smith, N., Notaro, A., Gaerlan, S., Kutschera, E., Kadash-Edmondson, K. E., Xing, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40083-6 · Cited by 38

Genomic variant benchmark: if you cannot measure it, you cannot improve it

Majidian, S., Agustinho, D. P., Chin, C.-S., Sedlazeck, F. J., & Mahmoud, M. (2023) · Genome biology · doi:10.1186/s13059-023-03061-1 · Cited by 35

scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data

Zhang, Z., Zhao, X., Bindra, M., Qiu, P., & Zhang, X. (2024) · Nature Communications · doi:10.1038/s41467-024-45227-w · Cited by 40

satuRn: Scalable analysis of differential transcript usage for bulk and single-cell RNA-sequencing applications

Gilis, J., Vitting‐Seerup, K., den Berge, K. V., & Clement, L. (2021) · F1000Research · doi:10.12688/f1000research.51749.1 · Cited by 34

Chromosome‐level genome assembly reveals genomic architecture of northern range expansion in the mountain pine beetle, Dendroctonus ponderosae Hopkins (Coleoptera: Curculionidae)

Keeling, C. I., Campbell, E. O., Batista, P. D., Shegelski, V. A., Trevoy, S. A. L., Huber, D. P. W., Janes, J. K., & Sperling, F. A. H. (2021) · Molecular Ecology Resources · doi:10.1111/1755-0998.13528 · Cited by 34

High-throughput Interpretation of Killer-cell Immunoglobulin-like Receptor Short-read Sequencing Data with PING

Marin, W. M., Dandekar, R., Augusto, D. G., Yusufali, T., Heyn, B., Hofmann, J. A., Lange, V., Sauter, J., Norman, P. J., & Hollenbach, J. A. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008904 · Cited by 36

PacBio sequencing output increased through uniform and directional fivefold concatenation

Kanwar, N., Blanco, C., Chen, I. A., & Seelig, B. (2021) · Scientific Reports · doi:10.1038/s41598-021-96829-z · Cited by 38

Microbial Identification Using rRNA Operon Region: Database and Tool for Metataxonomics with Long-Read Sequence

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