Genotype and SNP calling from next-generation sequencing data

The Sequence Alignment/Map format and SAMtools

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Fast and accurate short read alignment with Burrows-Wheeler transform

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

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Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.

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Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation

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A framework for variation discovery and genotyping using next-generation DNA sequencing data

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Velvet: Algorithms for de novo short read assembly using de Bruijn graphsPreprint

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A map of human genome variation from population-scale sequencing.

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dbSNP: the NCBI database of genetic variation

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Sequencing technologies – the next generation

Metzker, M. L. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2626 · Cited by 7,066

Future Prospects of Molecular Markers in Plants

Mir, R. R., & Varshney, R. K. (2012) · Wiley eBooks · doi:10.1002/9781118473023.ch10 · Cited by 38

Reference Genome Choice and Filtering Thresholds Jointly Influence Phylogenomic Analyses

Rick, J. A., Brock, C. D., Lewanski, A. L., Golcher‐Benavides, J., & Wagner, C. E. (2023) · Systematic Biology · doi:10.1093/sysbio/syad065 · Cited by 42

Identification of an EMS-induced causal mutation in a gene required for boron-mediated root development by low-coverage genome re-sequencing inArabidopsis

Tabata, R., Kamiya, T., Shigenobu, S., Yamaguchi, K., Yamada, M., Hasebe, M., Fujiwara, T., & Sawa, S. (2013) · Plant Signaling & Behavior · doi:10.4161/psb.22534 · Cited by 37

Re-Ranking Sequencing Variants in the Post-GWAS Era for Accurate Causal Variant Identification

Faye, L. L., Machiela, M. J., Kraft, P. L., Bull, S. B., & Sun, L. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003609 · Cited by 37

Genotyping-by-Sequencing for Populus Population Genomics: An Assessment of Genome Sampling Patterns and Filtering Approaches

Schilling, M. P., Wolf, P. G., Duffy, A. M., Rai, H. S., Rowe, C. A., Richardson, B. A., & Mock, K. E. (2014) · PLoS ONE · doi:10.1371/journal.pone.0095292 · Cited by 37

Exploiting genotyping by sequencing to characterize the genomic structure of the American cranberry through high-density linkage mapping

Covarrubias‐Pazaran, G., Diaz-Garcia, L. A., Schlautman, B., Deutsch, J., Salazar, W., Hernandez-Ochoa, M., Grygleski, E., Steffan, S. A., Iorizzo, M., Polashock, J. J., et al. (2016) · BMC Genomics · doi:10.1186/s12864-016-2802-3 · Cited by 37

Large-scale SNP discovery and construction of a high-density genetic map of Colossoma macropomum through genotyping-by-sequencing

Nunes, J. D. R. D. S., Liu, S., Pértille, F., Perazza, C. A., Villela, P. M. S., de Almeida‐Val, V. M. F., Hilsdorf, A. W. S., Liu, Z., & Coutinho, L. L. (2017) · Scientific Reports · doi:10.1038/srep46112 · Cited by 37

Gene Expression Modularity Reveals Footprints of Polygenic Adaptation in Theobroma cacao

Hämälä, T., Guiltinan, M. J., Marden, J. H., Maximova, S. N., dePamphilis, C. W., & Tiffin, P. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz206 · Cited by 37

Population-genomic analyses reveal bottlenecks and asymmetric introgression from Persian into iron walnut during domestication

Ding, Y., Cao, Y., Zhang, W., Chen, J., Liu, J., Li, P., Renner, S. S., Zhang, D., & Bai, W. (2022) · Genome biology · doi:10.1186/s13059-022-02720-z · Cited by 40

Capture-based high-coverage NGS: a powerful tool to uncover a wide spectrum of mutation types

Wang, J., Yu, H., Zhang, V. W., Xia, T., Feng, Y., Wang, G., Gorman, E. B., Wang, H., Lutz, R. E., Schmitt, E. S., et al. (2015) · Genetics in Medicine · doi:10.1038/gim.2015.121 · Cited by 37

DiscoSnp++ : de novo detection of small variants from raw unassembled read set(s)Preprint

Peterlongo, P., Riou, C., Drézen, E., & Lemaitre, C. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/209965 · Cited by 37

Bacterial Genomics in Infectious Disease and the Clinical Pathology Laboratory

Olsen, R. J., Long, S. W., & Musser, J. M. (2012) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2012-0025-ra · Cited by 36

Targeted Locus Amplification and Next-Generation Sequencing

Hottentot, Q. P., van Min, M., Splinter, E., & White, S. J. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-6442-0_13 · Cited by 36

Advances in DNA typing in the agro-food supply chain

Corrado, G. (2016) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2016.04.003 · Cited by 36

A review on the application of bioinformatics tools in food microbiome studies

Chelliah, R., Daliri, E. B., Khan, I., Wei, S., Elahi, F., Yeon, S., Vijayalakshmi, S., Ofosu, F. K., Rubab, M., Ju, H. H., et al. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac007 · Cited by 35

Estimation of site frequency spectra from low-coverage sequencing data using stochastic EM reduces overfitting, runtime, and memory usage

Rasmussen, M. S., Garcia‐Erill, G., Korneliussen, T. S., Wiuf, C., & Albrechtsen, A. (2022) · Genetics · doi:10.1093/genetics/iyac148 · Cited by 35

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

High‐throughput sequencing of the melanoma genome

Kunz, M., Dannemann, M., & Kelso, J. F. (2012) · Experimental Dermatology · doi:10.1111/exd.12054 · Cited by 36

Genomic comparisons reveal biogeographic and anthropogenic impacts in the koala (Phascolarctos cinereus): a dietary-specialist species distributed across heterogeneous environments

Kjeldsen, S. R., Raadsma, H. W., Leigh, K. A., Tobey, J. R., Phalen, D. N., Krockenberger, A. K., Ellis, W. A. H., Hynes, E. F., Higgins, D. P., & Zenger, K. R. (2018) · Heredity · doi:10.1038/s41437-018-0144-4 · Cited by 35

SNPeffect: identifying functional roles of SNPs using metabolic networks

Sarkar, D., & Maranas, C. D. (2020) · The Plant Journal · doi:10.1111/tpj.14746 · Cited by 35

Associations between DNA methylation and gene regulation depend on chromatin accessibility during transgenerational plasticity

Bogan, S. N., Strader, M. E., & Hofmann, G. E. (2023) · BMC Biology · doi:10.1186/s12915-023-01645-8 · Cited by 37

Reference-free SNP calling: improved accuracy by preventing incorrect calls from repetitive genomic regions

Dou, J., Zhao, X., Fu, X., Jiao, W., Wang, N., Zhang, L., Hu, X., Wang, S., & Bao, Z. (2012) · Biology Direct · doi:10.1186/1745-6150-7-17 · Cited by 35

A gradient-boosting approach for filtering de novo mutations in parent–offspring trios

Liu, Y., Li, B., Tan, R., Zhu, X., & Wang, Y. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu141 · Cited by 35

Impact of post-alignment processing in variant discovery from whole exome data

Tian, S., Yan, H., Kalmbach, M. T., & Slager, S. L. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1279-z · Cited by 35

The genetic legacy of 50 years of desert bighorn sheep translocations

Jahner, J. P., Matocq, M. D., Malaney, J. L., Cox, M., Wolff, P. L., Gritts, M. A., & Parchman, T. L. (2018) · Evolutionary Applications · doi:10.1111/eva.12708 · Cited by 35

Variant Calling from RNA-seq Data Using the GATK Joint Genotyping Workflow

Brouard, J., & Bissonnette, N. (2012) · Methods in molecular biology · doi:10.1007/978-1-0716-2293-3_13 · Cited by 34

Using Mendelian Inheritance To Improve High-Throughput SNP Discovery

Chen, N. X., Hout, C. V. V., Gottipati, S., & Clark, A. G. (2014) · Genetics · doi:10.1534/genetics.114.169052 · Cited by 34

Exploring variation-aware contig graphs for (comparative) metagenomics using MaryGold

Nijkamp, J. F., Pop, M., Reinders, M. J. T., & de Ridder, D. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt502 · Cited by 34

Advanced variant classification framework reduces the false positive rate of predicted loss-of-function variants in population sequencing data

Singer‐Berk, M., Gudmundsson, S., Baxter, S. M., Seaby, E. G., England, E. M., Wood, J. C., Son, R. G., Watts, N. A., Karczewski, K. J., Harrison, S. M., et al. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.08.005 · Cited by 33

Genome-wide selection signatures address trait specific candidate genes in cattle indigenous to arid regions of India

Sukhija, N., Malik, A. A., Devadasan, M. J., Dash, A., Bidyalaxmi, K., Kumar, D. R., Devi, M. K., Choudhary, A., Kanaka, K. K., Sharma, R., et al. (2023) · Animal Biotechnology · doi:10.1080/10495398.2023.2290521 · Cited by 33

Construction of a high-resolution genetic map of Crassostrea gigas: QTL mapping and GWAS applications revealed candidate genes controlling nutritional traits

Shi, R., Li, C., Qi, H., Liu, S., Wang, W., Li, L., & Zhang, G. (2020) · Aquaculture · doi:10.1016/j.aquaculture.2020.735427 · Cited by 33

How Well Do Molecular and Pedigree Relatedness Correspond, in Populations with Diverse Mating Systems, and Various Types and Quantities of Molecular and Demographic Data?

Kopps, A. M., Kang, J., Sherwin, W. B., & Palsbøll, P. J. (2015) · G3 Genes Genomes Genetics · doi:10.1534/g3.115.019323 · Cited by 33

Forward Genetics by Sequencing EMS Variation-Induced Inbred Lines

Addo‐Quaye, C., Buescher, E., Best, N. B., Chaikam, V., Baxter, I. R., & Dilkes, B. P. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.116.029660 · Cited by 33

A pooling-based approach to mapping genetic variants associated with DNA methylationPreprint

Kaplow, I. M., MacIsaac, J. L., Mah, S. M., McEwen, L. M., Kobor, M. S., & Fraser, H. B. (2015) · Genome Research · doi:10.1101/gr.183749.114 · Cited by 38

Choice of reference-guided sequence assembler and SNP caller for analysis of Listeria monocytogenes short-read sequence data greatly influences rates of error

Pightling, A., Petronella, N., & Pagotto, F. (2015) · BMC Research Notes · doi:10.1186/s13104-015-1689-4 · Cited by 32

Strategies for identification of mutations induced by carbon-ion beam irradiation in Arabidopsis thaliana by whole genome re-sequencing

Du, Y., Luo, S., Yu, L., Cui, T., Chen, X., Yang, J.-Y., Li, X., Li, W., Wang, J., & Zhou, L. (2017) · Mutation research. Fundamental and molecular mechanisms of mutagenesis · doi:10.1016/j.mrfmmm.2017.12.001 · Cited by 32

Genome projects in invasion biology

McCartney, M. A., Mallez, S., & Gohl, D. M. (2019) · Conservation Genetics · doi:10.1007/s10592-019-01224-x · Cited by 32

Population structure, genetic diversity and linkage disequilibrium in a macadamia breeding population using SNP and silicoDArT markers

O'Connor, K., Kilian, A., Hayes, B. J., Hardner, C. M., Nock, C. J., Baten, A., Alam, M. M., & Topp, B. L. (2019) · Tree Genetics & Genomes · doi:10.1007/s11295-019-1331-z · Cited by 32

Systems Analysis of High-Throughput Data

Braun, R. (2014) · Advances in experimental medicine and biology · doi:10.1007/978-1-4939-2095-2_8 · Cited by 31

DNA polymerase-catalyzed incorporation of nucleotides modified with a G-quadruplex-derived DNAzyme

Verga, D., Welter, M., Steck, A., & Marx, A. (2015) · Chemical Communications · doi:10.1039/c5cc01387a · Cited by 31

Efficiency and Power as a Function of Sequence Coverage, SNP Array Density, and Imputation

Flannick, J. A., Korn, J. M., Fontanillas, P., Grant, G., Banks, E., DePristo, M. A., & Altshuler, D. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002604 · Cited by 32

Chronic radiation exposure at Chernobyl shows no effect on genetic diversity in the freshwater crustacean, Asellus aquaticus thirty years on

Fuller, N. W., Ford, A. T., Lerebours, A., Gudkov, D. I., Nagorskaya, L. L., & Smith, J. T. (2019) · Ecology and Evolution · doi:10.1002/ece3.5478 · Cited by 31

Large-scale inference of population structure in presence of missingness using PCA

Meisner, J., Liu, S. M., Huang, M., & Albrechtsen, A. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab027 · Cited by 33

Differential performance of strategies for single-cell whole-genome amplification

Estévez‐Gómez, N., Prieto, T., Tomás, L., Alvariño, P., Guillaumet-Adkins, A., Heyn, H., Prado‐Lòpez, S., & Posada, D. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101025 · Cited by 32

Discovery and validation of genic single nucleotide polymorphisms in the Pacific oyster Crassostrea gigas

Wang, J., Qi, H., Li, L., Que, H., Wang, D., & Zhang, G. (2014) · Molecular Ecology Resources · doi:10.1111/1755-0998.12278 · Cited by 31

Discovery and characterization of Alu repeat sequences via precise local read assembly

Halo, J. V., Baron, A., Diroff, N. M., & Kidd, J. M. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1089 · Cited by 31

Increasing Genome Sampling and Improving SNP Genotyping for Genotyping-by-Sequencing with New Combinations of Restriction Enzymes

Fu, Y., Peterson, G. W., & Dong, Y. (2016) · G3 Genes Genomes Genetics · doi:10.1534/g3.115.025775 · Cited by 31

Technical Advances in the Measurement of Residual Disease in Acute Myeloid Leukemia

Roloff, G. W., Lai, C., Hourigan, C. S., & Dillon, L. W. (2017) · Journal of Clinical Medicine · doi:10.3390/jcm6090087 · Cited by 32

A high-performance computational workflow to accelerate GATK SNP detection across a 25-genome dataset

Zhou, Y., Kathiresan, N., Yu, Z., Rivera, L. F., Yang, Y., Thimma, M., Manickam, K., Chebotarov, D., Mauleon, R., Chougule, K. M., et al. (2024) · BMC Biology · doi:10.1186/s12915-024-01820-5 · Cited by 31

Building a Genome Analysis Pipeline to Predict Disease Risk and Prevent Disease

Bromberg, Y. (2013) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.07.038 · Cited by 31

EVA: Exome Variation Analyzer, an efficient and versatile tool for filtering strategies in medical genomics

Coutant, S., Cabot, C., Lefebvre, A., Léonard, M., Prieur-Gaston, É., Campion, D., Thierry Lecroq, B., & Dauchel, H. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s14-s9 · Cited by 35

A New Genotype Imputation Method with Tolerance to High Missing Rate and Rare Variants

Yang, Y., Wang, Q., Chen, Q., Liao, R., Zhang, X., Yang, H., Zheng, Y., Zhang, Z., & Pan, Y. (2014) · PLoS ONE · doi:10.1371/journal.pone.0101025 · Cited by 31

Testing Rare-Variant Association without Calling Genotypes Allows for Systematic Differences in Sequencing between Cases and Controls

Hu, Y., Liao, P., Johnston, H. R., Allen, A. S., & Satten, G. A. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006040 · Cited by 35

High-Density Genetic Map Construction and Stem Total Polysaccharide Content-Related QTL Exploration for Chinese Endemic Dendrobium (Orchidaceae)

Lu, J., Liu, Y., Xu, J., Mei, Z., Shi, Y., Liu, P., He, J., Wang, X., Meng, Y., Feng, S., et al. (2018) · Frontiers in Plant Science · doi:10.3389/fpls.2018.00398 · Cited by 30

Comparing inferences derived from microsatellite and RADseq datasets: a case study involving threatened bull trout

Bohling, J. H., Small, M. P., Bargen, J. V., Louden, A., & DeHaan, P. W. (2019) · Conservation Genetics · doi:10.1007/s10592-018-1134-z · Cited by 30

Whole‐exome sequencing reveals a long‐term decline in effective population size of red spruce (Picea rubens)

Capblancq, T., Butnor, J. R., Deyoung, S., Thibault, E., Munson, H., Nelson, D. M., Fitzpatrick, M. C., & Keller, S. R. (2020) · Evolutionary Applications · doi:10.1111/eva.12985 · Cited by 30

Development and comparison of RNA-sequencing pipelines for more accurate SNP identification: practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle

Lam, S., Zeidan, J., Miglior, F., Suárez‐Vega, A., Gómez‐Redondo, I., Fonseca, P. A. D. S., Guan, L. L., Waters, S. M., & Cánovas, Á. (2020) · BMC Genomics · doi:10.1186/s12864-020-07107-7 · Cited by 30

Whole exome sequencing identifies the potential for genetic rescue in iconic and critically endangered Panamanian harlequin frogs

Byrne, A. Q., Richards‐Zawacki, C. L., Voyles, J., Bi, K., Ibáñez, R., & Rosenblum, E. B. (2020) · Global Change Biology · doi:10.1111/gcb.15405 · Cited by 32

Population structure, genomic diversity and demographic history of Komodo dragons inferred from whole‐genome sequencing

Iannucci, A., Benazzo, A., Natali, C., Arida, E. A., Zein, M. S. A., Jessop, T. S., Bertorelle, G., & Ciofi, C. (2021) · Molecular Ecology · doi:10.1111/mec.16121 · Cited by 32

An empirical evaluation of genotype imputation of ancient DNA

Ausmees, K., Sánchez‐Quinto, F., Jakobsson, M., & Nettelblad, C. (2022) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkac089 · Cited by 32

Emergent biomarker derived from next-generation sequencing to identify pain patients requiring uncommonly high opioid doses

Kringel, D., Ultsch, A., Zimmermann, M., Jansen, J.-P., Ilias, W., Freynhagen, R., Grießinger, N., Köpf, A., Stein, C., Doehring, A., et al. (2016) · The Pharmacogenomics Journal · doi:10.1038/tpj.2016.28 · Cited by 30

Molecular diagnosis of autosomal dominant polycystic kidney disease

Song, X., Haghighi, A., Iliuta, I.-A., & Pei, Y. (2017) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2017.1358088 · Cited by 30

Bridging Genomics and Phenomics

Chen, D., Chen, M., Altmann, T., & Klukas, C. (2013) · Springer eBooks · doi:10.1007/978-3-642-41281-3_11 · Cited by 31

Whole Genome Sequencing Analysis

Yin, R., Kwoh, C. K., & Zheng, J. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.20095-2 · Cited by 30

High-throughput and sensitive next-generation droplet digital PCR assay for the quantitation of the hepatitis C virus mutation at core amino acid 70

Mukaide, M., Sugiyama, M., Korenaga, M., Murata, K., Kanto, T., Masaki, N., & Mizokami, M. (2014) · Journal of Virological Methods · doi:10.1016/j.jviromet.2014.07.006 · Cited by 29

Biases and Errors on Allele Frequency Estimation and Disease Association Tests of Next‐Generation Sequencing of Pooled Samples

Chen, X., Listman, J. B., Slack, F. J., Gelernter, J. E., & Zhao, H. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21648 · Cited by 29

A MAT1–2 wild‐type strain from P enicillium chrysogenum: functional mating‐type locus characterization, genome sequencing and mating with an industrial penicillin‐producing strain

Böhm, J. W., Dahlmann, T. A., Gümüşer, H., & Kück, U. (2014) · Molecular Microbiology · doi:10.1111/mmi.12909 · Cited by 32

Codweb: Whole-genome sequencing uncovers extensive reticulations fueling adaptation among Atlantic, Arctic, and Pacific gadids

Árnason, E., & Halldórsdóttir, K. (2019) · Science Advances · doi:10.1126/sciadv.aat8788 · Cited by 29

Genomic relationships computed from either next‐generation sequence or array SNP data

Miguel Pérez-enciso, A. (2014) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12074 · Cited by 32

Population and genomic lessons from genetic analysis of two Indian populations

Juyal, G., Mondal, M., Luisi, P., Laayouni, H., Sood, A., Midha, V., Heutink, P., Bertranpetit, J., Thelma, B. K., & Casals, F. (2014) · Human Genetics · doi:10.1007/s00439-014-1462-0 · Cited by 30

Using VAAST to Identify Disease‐Associated Variants in Next‐Generation Sequencing Data

Kennedy, B., Kronenberg, Z. N., Hu, H., Moore, B., Flygare, S. D., Reese, M. G., Jorde, L. B., Yandell, M. D., & Huff, C. D. (2014) · Current Protocols in Human Genetics · doi:10.1002/0471142905.hg0614s81 · Cited by 29

Transcriptome altered by latent human cytomegalovirus infection on THP-1 cells using RNA-seq

Zhang, Q., Lai, M., Lou, Y.-Y., Guo, B., Wang, H., & Zheng, X. (2016) · Gene · doi:10.1016/j.gene.2016.09.014 · Cited by 30

Ultra-low-density genotype panels for breed assignment of Angus and Hereford cattle

Judge, M. M., Kelleher, M., Kearney, J. F., Sleator, R. D., & Berry, D. P. (2016) · animal · doi:10.1017/s1751731116002457 · Cited by 29

An ABC Method for Whole-Genome Sequence Data: Inferring Paleolithic and Neolithic Human Expansions

Jay, F., Boitard, S., & Austerlitz, F. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz038 · Cited by 35

FixItFelix: improving genomic analysis by fixing reference errors

Behera, S., LeFaive, J., Orchard, P., Mahmoud, M., Paulin, L. F., Farek, J., Soto, D. C., Parker, S. C. J., Smith, A. V., Dennis, M. Y., et al. (2023) · Genome Biology · doi:10.1186/s13059-023-02863-7 · Cited by 28

A computational approach for positive genetic identification and relatedness detection from low-coverage shotgun sequencing data

Nguyen, R., Kapp, J. D., Sacco, S., Myers, S. P., & Green, R. E. (2023) · Journal of Heredity · doi:10.1093/jhered/esad041 · Cited by 28

Comparative genomics of Brassicaceae crops

Sharma, A. K., Li, X., & Lim, Y. P. (2014) · Ikushugaku zasshi · doi:10.1270/jsbbs.64.3 · Cited by 28

Whole Exome Sequencing Implicates an INO80D Mutation in a Syndrome of Aortic Hypoplasia, Premature Atherosclerosis, and Arterial Stiffness

Shameer, K., Klee, E. W., Dalenberg, A. K., & Kullo, I. J. (2014) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.113.000233 · Cited by 28

Targeted Sequencing Reveals Large-Scale Sequence Polymorphism in Maize Candidate Genes for Biomass Production and Composition

Muraya, M. M., Schmutzer, T., Ulpinnis, C., Scholz, U., & Altmann, T. (2015) · PLoS ONE · doi:10.1371/journal.pone.0132120 · Cited by 28

SNVSniffer: an integrated caller for germline and somatic single-nucleotide and indel mutations

Liu, Y., Löewer, M., Aluru, S., & Schmidt, B. (2016) · BMC Systems Biology · doi:10.1186/s12918-016-0300-5 · Cited by 28

An analytical workflow for accurate variant discovery in highly divergent regions

Tian, S., Yan, H., Neuhauser, C. M., & Slager, S. L. (2016) · BMC Genomics · doi:10.1186/s12864-016-3045-z · Cited by 28

Promises and pitfalls of Illumina sequencing for HIV resistance genotyping

Brumme, C. J., & Poon, A. F. Y. (2016) · Virus Research · doi:10.1016/j.virusres.2016.12.008 · Cited by 28

Analysis of Genetic Diversity and Population Structure in Three Forest Musk Deer Captive Populations with Different Origins

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Label-Free Detection of C–T Mutations by Surface-Enhanced Raman Spectroscopy Using Thiosulfate-Modified Nanoparticles

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