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NGSEP

(Next Generation Sequencing Experience Platform)

NGSEP is an integrated framework for analysis of high throughput sequencing (HTS) reads. The current version of NGSEP includes functionalities for the following main tasks:
1. Genome assembly from long reads
2. Library-guided annotation of transposable elements in a genome assembly
3. Ortholog identification and alignment of annotated genome assemblies
4. Mapping of long and short reads.
5. Construction and downstream analysis of large datasets of genomic variation from reads aligned to a reference genome.
6. De-novo analysis of Genotype-by-Sequencing reads which supports single-end and paired end GBS and ddRAD-seq data.

For our latest news, follow us on Twitter: @NGSEP

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Complete list of functionalities

De-novo and reference guided reads processing

1. Read demultiplexing
2. Spectrum of k-mer abundances from fastq or fasta files
3. Raw reads error correction
4. De-novo assembly of long reads
5. NEW Sorting of genome assemblies by comparison to a close reference genome
6. NEW Circularization of circular molecules in genome assemblies
7. Construction of a haploid genome for a sequenced individual from homozygous alternative variants (assembly polishing)
8. De-novo analysis of Genotype-by-sequencing (GBS) reads
9. Alignment of raw reads to a reference genome

Variants discovery and genotyping

1. Integrated analysis of multiple samples for efficient discovery and genotyping of SNVs, indels and STRs. This is the recommended option for GBS, RAD-sequencingExome sequencing, RNA-seq and low coverage WGS data
2. Complete individual sample analysis for discovery and genotyping of SNVs, indels, STRs, and CNVs from WGS data
3. Merging of genotype calls from different samples into a single VCF file
4. Molecular haplotyping of single individuals
5. Base pair quality and coverage statistics
6. Distribution of relative allele counts from BAM files
7. Genome-wide comparison of read depth patterns between two samples
8. Individual identification (genotyping) for variants identified from pools in TILLING experiments

Analysis of annotated gene models and transcripts

1. Analysis of structural genome annotations in GFF3 format
2. Filtering of transcriptome files in GFF3 format
3. Large scale alignment of two assembled and annotated genomes.
4. Identification and clustering of orthologs and paralogs
5. Library-guided annotation of transposable elements in a genome assembly.
6. Masking of genome assemblies.

Variants (VCF) downstream analysis

1. Functional annotation of genomic variants.
2. Filtering of VCF files using quality, coverage, and functional criteria.
3. Conversion of VCF files to input formats for several downstream analysis tools such as Splitstree, Structure, PowerMarker, Flapjack or Tassel.
4. Comparison of genotype calls between VCF files.
5. Calculation of IBS distance matrices from VCF files.
6. Neighbor joining clustering from distance matrices.
7. Genotype imputation.
8. Calculation of variant density across the genome.
9. Allele sharing statistics for inbred populations.
10. A window-based analysis to discover haplotype introgressions from population VCF files.
11. Alignment of consensus sequences and translation of coordinates for variants obtained with the de-novo analysis of GBS reads.

Benchmark and simulation

1. Simulation of single individuals with different ploidies and variants from a reference genome.
2. Simulation of single reads.
3. Benchmark statistics comparing test and gold standard VCF files.
4. Simulation of TILLING experiments.

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Downloads

Command Line Executable :

Library to be used in a terminal (Including README for usage instructions).
Source Code bundle for compiling.

Graphical interface :

Since version 4 we built a new graphical interface. This package has been tested on Linux, MAC and Windows having as only prerrequisite the Java Runtime Environment v11. Unlike the Eclipse plugin, this package does not require any external application. A user manual for this interface can be accessed here.

The Eclipse plugin will no longer be sustained. However, the plugin with the functionalities of NGSEP 3 is still available. The package including Eclipse and an installer of bowtie2 is also available. To update an existing installation, just download the plugin and follow the instructions in the User Manual.

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Documentation

Resources for the command line:
1 Quick Start to get familiar with the pipeline for read alignment and variants detection.
2 Tutorial to learn step-by-step how to use NGSEP for read alignment and variants detection.
3 Bash Scripts to make things faster.
4 README with a detailed description of all modules and parameters.
5. See what's new in the Change Log.

• User manual for the new graphical interface (under construction)

• User Manual of version 3 for the legacy Eclipse plugin

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Support

For frequently asked questions, you can visit our discussion forum. Feel free to post your questions on the forum. You can also write to ja.duitama at uniandes.edu.co for more specific issues.

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Citing

The manuscript of NGSEP 4, focused on orthologs and genome alignment is available at Molecular Ecology Resources:

Tello D, Gonzalez-Garcia LN, Gomez J, et al. (2023).
NGSEP 4: Efficient and accurate identification of orthogroups and whole-genome alignment.
Molecular Ecology Resources 23(3): 712-724. https://doi.org/10.1111/1755-0998.13737

The manuscript describing the new functionality of NGSEP for de-novo genome assembly of long reads is available at Life Science Alliance:

Gonzalez-Garcia L, Guevara-Barrientos D, Lozano-Arce D et al. (2023).
New algorithms for accurate and efficient de novo genome assembly from long DNA sequencing reads.
Life Science Alliance 6(5): . http://doi.org/10.26508/lsa.202201719

The manuscript with the description of the initial modules of NGSEP is available at Nucleic Acids research:

Duitama J, Quintero JC, Cruz DF, Quintero C, Hubmann G, Foulquie-Moreno MR, Verstrepen KJ, Thevelein JM, and Tohme J. (2014).
An integrated framework for discovery and genotyping of genomic variants from high-throughput sequencing experiments.
Nucleic Acids Research 42(6): e44. http://doi.org/10.1093/nar/gkt1381

Details of different algorithms implemented in NGSEP can be found in different publications. Feel free to cite the most appropriate paper(s) depending on the analysis task(s) for which NGSEP was helpful :

Variants detection and genotyping:

The latest algorithms implemented in NGSEP 3 to improve accuracy for variants detection and genotyping can be found at Bioinformatics:

Tello D, Gil J, Loaiza CD, Riascos JJ, Cardozo N, and Duitama J. (2019)
NGSEP3: accurate variant calling across species and sequencing protocols.
Bioinformatics 35(22): 4716–4723. http://doi.org/10.1093/bioinformatics/btz275

Transposable elements:

Our approach to map known transposable elements to a genome assembly, based on minimizers can be found in Applications in Plant Sciences

Gonzalez-García LN, Lozano-Arce D, Londoño JP, Guyot R and Duitama J. (2023).
Efficient homology-based annotation of transposable elements using minimizers.
Applications in Plant Sciences 11(4): e11520. http://doi.org/10.1002/aps3.11520

Structural variants detection:

For long reads, our approach based on the DBScan clustering algorithm can be found in GigaScience

Gaitán N and Duitama J. (2024)
A graph clustering algorithm for detection and genotyping of structural variants from long reads
GigaScience 13: giad112. https://doi.org/10.1093/gigascience/giad112

For short reads, since version 2.1.2, we implemented an algorithm to integrate paired-end and split-read analysis for detection of large indels. Benchmark experiments of this algorithm against other software tools using data from the 3000 rice genomes project is available at Genome Research:

Fuentes RR, Chebotarov D, Duitama J, Smith S, De la Hoz JF, Mohiyuddin M, et al. (2019).
Structural variants in 3000 rice genomes.
Genome Research 29: 870-880. http://doi.org/10.1101/gr.241240.118

TILLING:

Functionalities related to the TILLING experimental setup can be found in Frontiers in Genetics:

Gil J, Andrade-Martínez JS and Duitama J. (2021)
Accurate, Efficient and User-Friendly Mutation Calling and Sample Identification for TILLING Experiments.
Frontiers in Genetics 12: 54. http://doi.org/10.3389/fgene.2021.624513

GBS pipelines:

Further details on the pipeline built for reference-guided variants detection on Genotype-By-Sequencing (GBS) data can be found at BMC Genomics:

Perea C, Hoz JFDL, Cruz DF, Lobaton JD, Izquierdo P, Quintero JC, Raatz B and Duitama J. (2016).
Bioinformatic analysis of genotype by sequencing (GBS) data with NGSEP.
BMC Genomics 17:498. http://doi.org/10.1186/s12864-016-2827-7

The manuscript describing the functionality to perform de-novo analysis of GBS reads can be found at Molecular Ecology Resources:

Parra-Salazar A, Gomez J, Lozano-Arce D, Reyes-Herrera PH and Duitama J. (2022).
Robust and efficient software for reference-free genomic diversity analysis of GBS data on diploid and polyploid species
Molecular Ecology Resources 22(1): 439-454. http://doi.org/10.1101/2020.11.28.402131

Molecular haplotyping:

Duitama J, McEwen GK, Huebsch T, Palczewski S, Schulz S, Verstrepen K, et al. (2011)
Fosmid-based whole genome haplotyping of a HapMap trio child: evaluation of Single Individual Haplotyping techniques.
Nucleic Acids Research 40(5):2041-2053. http://doi.org/10.1093/nar/gkr1042 PMID: 22102577

CNV detection (Read depth analysis):

Abyzov, A., Urban, A. E., Snyder, M., and Gerstein, M. (2011).
CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing.
Genome research, 21(6), 974–84. http://doi.org/10.1101/gr.114876.110

Yoon S, Xuan Z, Makarov V, Ye K, Sebat J. (2009).
Sensitive and accurate detection of copy number variants using read depth of coverage.
Genome Research Sep;19(9):1586-1592. http://doi.org/10.1101/gr.092981.109

Genotype imputation:

Scheet, P and Stephens, M. (2006).
A Fast and Flexible Statistical Model for Large-Scale Population Genotype Data: Applications to Inferring Missing Genotypes and Haplotypic Phase.
American Journal of Human Genetics 78: 629-644. http://doi.org/10.1086/502802

Read Depth comparison:

Xie C and Tammi MT. (2009).
CNV-seq, a new method to detect copy number variation using high-throughput sequencing.
BMC Bioinformatics 10:80. http://doi.org/10.1186/1471-2105-10-80

Haplotype introgression analysis:

Duitama J, Silva A, Sanabria Y, Cruz DF, Quintero C, Ballen C, et al. (2015)
Whole Genome Sequencing of Elite Rice Cultivars as a Comprehensive Information Resource for Marker Assisted Selection.
PLoS ONE 10(4): e0124617. http://doi.org/10.1371/journal.pone.0124617

NGSEP is also supported by the following open source software packages:

• hts-jdk: Insfrastructure to read and write SAM, BAM and CRAM files.
• Picard: Used in the graphical interface to sort SAM or BAM files
• Jsci: Math functions and statistical distributions
• Trimmomatic: We borrowed one class from Trimmomatic 0.35 to allow correct reading of large gzip files

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Publications citing NGSEP

Different modules of NGSEP have been used to perform bioinformatic analysis in more than 100 scientific publications:

Amongi W, Nkalubo ST, Ochwo-Ssemakula M, et al. (2023).
Genetic clustering, and diversity of African panel of released common bean genotypes and breeding lines.
Genetic Resources and Crop Evolution in press. https://doi.org/10.1007/s10722-023-01559-y

Vega M, Quintero-Correales C, Mastretta-Yanes A, et al. (2023).
Multiple domestication events explain the origin of Gossypium hirsutum landraces in Mexico
Ecology and Evolution 13(3): e9838

Ethridge SR, Chandra S, Everman W, et al. (2023).
Rapid evolution of competitive ability in giant foxtail (Setaria faberi) over 34 years.
Weed Science 71(1): 59-68. http://doi.org/10.1017/wsc.2023.1

De Souza Rodrigues Marinho J, Valdisser PAMR, Brondani C, et al. (2023).
Molecular markers for assessing the inter- and intra-racial genetic diversity and structure of common bean.
Genetic Resources and Crop Evolution 70: 263–279. https://doi.org/10.1007/s10722-022-01432-4

Farek J, Hughes D, Salerno W, et al. (2023).
xAtlas: scalable small variant calling across heterogeneous next-generation sequencing experiments.
GigaScience 12: giac125. https://doi.org/10.1093/gigascience

Kang H, An S-M, Park Y-J, et al. (2023).
Population Genomics Study and Implications for the Conservation of Zabelia tyaihyonii Based on Genotyping-By-Sequencing.
Plants 12(1):171. https://doi.org/10.3390/plants12010171

García Navarrete T, Arias C, Mukundi E, et al. (2022).
Natural variation and improved genome annotation of the emerging biofuel crop field pennycress (Thlaspi arvense).
G3 12(6): jkac084. https://doi.org/10.1093/g3journal/jkac084

He T, Ye C, Zeng Q, et al. (2022).
Genetic diversity and population structure of cultivated Dendrobium nobile Lindl. in southwest of China based on genotyping-by-sequencing.
Genetic Resources and Crop Evolution 69: 2803–2818. https://doi.org/10.1007/s10722-022-01401-x

Muli JK, Neondo JO, Kamau PK, et al. (2022).
Genetic diversity and population structure of wild and cultivated Crotalaria species based on genotyping-by-sequencing.
PLoS ONE 17(9): e0272955. https://doi.org/10.1371/journal.pone.0272955

Lui C, Wang Y, Peng J, et al. (2022)
High-quality genome assembly and pan-genome studies facilitate genetic discovery in mung bean and its improvement.
Plant Communications 3 (6): 100352. https://doi.org/10.1016/j.xplc.2022.100352.

Upadhyay P, Gupta M, Sra SK, et al. (2022).
Genome wide association studies for acid phosphatase activity at varying phosphorous levels in Brassica juncea L.
Frontiers in Plant Sciences 13:1056028. http://doi.org/10.3389/fpls.2022.1056028

Barrera S, Teran JCBM, Lobaton JD, et al. (2022).
Large genomic introgression blocks of Phaseolus parvifolius Freytag bean into the common bean enhance the crossability between tepary and common beans.
Plant direct 6(12): e470. https://doi.org/10.1002/pld3.470

Keller B, Ariza-Suarez D, Portilla-Benavides AE, et al. (2022).
Improving Association Studies and Genomic Predictions for Climbing Beans With Data From Bush Bean Populations.
Frontiers in Plant Sciences 13:830896. http://doi.org/10.3389/fpls.2022.830896

Velez N, Vega-Vela N, Muñoz M, et al. (2022).
Deciphering the Association among Pathogenicity, Production and Polymorphisms of Capsule/Melanin in Clinical Isolates of Cryptococcus neoformans var. grubii VNI.
Journal of Fungi 8(3): 245. https://doi.org/10.3390/jof8030245

Saballos A, Soler-Garzón A, Brooks M, et al. (2022).
Multiple Genomic Regions Govern Tolerance to Sulfentrazone in Snap Bean (Phaseolus Vulgaris L.).
Frontiers in Agronomy 4:869770. http://doi.org/10.3389/fagro.2022.869770

Sadohara R, Izquierdo P, Couto Alves F. et al. (2022).
The Phaseolus vulgaris L. Yellow Bean Collection: genetic diversity and characterization for cooking time.
Genetic Resources and Crop Evolution 69: 1627–1648. https://doi.org/10.1007/s10722-021-01323-0

Diaz S, Polania J, Ariza-Suarez D, et al. (2022).
Genetic Correlation Between Fe and Zn Biofortification and Yield Components in a Common Bean (Phaseolus vulgaris L.).
Frontiers in Plant Science 12:739033. http://doi.org/10.3389/fpls.2021.739033

Souffriau B, Holt S, Hagman A, et al. (2022).
Polygenic Analysis of Tolerance to Carbon Dioxide Inhibition of Isoamyl Acetate “Banana” Flavor Production in Yeast Reveals MDS3 as Major Causative Gene.
Applied and Environmental Microbiology 88 (18): e0081422. http://doi.org/10.1128/aem.00814-22.

Ariza-Suarez D, Keller B, Spescha A, et al. (2022).
Genetic analysis of resistance to bean leaf crumple virus identifies a candidate LRR-RLK gene.
The plant journal 114(1): 23-38. https://doi.org/10.1111/tpj.15810

Stojiljkovic M, Claes A, Deparis Q, et al. (2022).
Whole-Genome Transformation of Yeast Promotes Rare Host Mutations with a Single Causative SNP Enhancing Acetic Acid Tolerance.
Molecular and Cellular Biology 42(4): e00560-21. https://doi.org/10.1128/mcb.00560

Byrne T, Farrelly N, Kelleher C, et al. (2022).
Genetic Diversity and Structure of a Diverse Population of Picea sitchensis Using Genotyping-by-Sequencing.
Forests 13(9):1511. https://doi.org/10.3390/f13091511

Heredia-Pech M, Chávez-Pesqueira M, Ortiz-García MM, t al. (2022).
Consequences of introgression and gene flow on the genetic structure and diversity of Lima bean (Phaseolus lunatus L.) in its Mesoamerican diversity area.
PeerJ 10:e13690 https://doi.org/10.7717/peerj.13690

Weisweiler M, Arlt C, Wu PY, et al. (2022).
Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation.
Theoretical and Applied Genetics 135: 3511–3529. https://doi.org/10.1007/s00122-022-04197-7

Liu G, Bracco A, Quattrini AM and Herrera S. (2021).
Kilometer-Scale Larval Dispersal Processes Predict Metapopulation Connectivity Pathways for Paramuricea biscaya in the Northern Gulf of Mexico.
Frontiers in Marine Science 8:790927. http://doi.org/10.3389/fmars.2021.790927

Garcia T, Duitama J, Zullo SS, et al. (2021).
Comprehensive genomic resources related to domestication and crop improvement traits in Lima bean.
Nature communications 12: 702. http://doi.org/10.1038/s41467-021-20921-1

Sadohara R, Long Y, Izquierdo P, Urrea CA, Morris D and Cichy K. (2021).
Seed coat color genetics and genotype × environment effects in yellow beans via machine-learning and genome-wide association.
The Plant Genome in press. https://doi.org/10.1002/tpg2.20173

Galaska MP, Liu G, West D, et al. (2021).
Seascape Genomics Reveals Metapopulation Connectivity Network of Paramuricea biscaya in the Northern Gulf of Mexico.
Frontiers in Marine Science 8:790929. https://doi.org/10.3389/fmars.2021.790929

Sánchez-Corrales L, Tovar-Aguirre OL, Galeano-Vanegas NF, et al. (2021).
Phylogenomic analysis and Mycobacterium tuberculosis antibiotic resistance prediction by whole-genome sequencing from clinical isolates of Caldas, Colombia.
PLoS ONE 16(10): e0258402. https://doi.org/10.1371/journal.pone.0258402

Duk M, Kanapin A, Rozhmina T, et al. (2021).
The Genetic Landscape of Fiber Flax.
Frontiers in Plant Science 12:764612. http://doi.org/10.3389/fpls.2021.764612

Diaz S, Ariza-Suarez D, Ramdeen R, et al. (2021).
Genetic Architecture and Genomic Prediction of Cooking Time in Common Bean (Phaseolus vulgaris L.).
Frontiers in Plant Science 11:622213. https://doi.org/10.3389/fpls.2020.622213

Soler-Garzón A, Oladzad A, Beaver J, et al. (2021).
NAC Candidate Gene Marker for bgm-1 and Interaction With QTL for Resistance to Bean Golden Yellow Mosaic Virus in Common Bean.
Frontiers in Plant Science 12:628443. https://doi.org/10.3389/fpls.2021.628443

Garreta L, Cerón-Souza I, Palacio MR, Reyes-Herrera PH. (2021).
MultiGWAS: An integrative tool for Genome Wide Association Studies in tetraploid organisms.
Ecology and Evolution 11(12): 7411-7426. https://doi.org/10.1002/ece3.7572

Medina C.A., Yu LX. (2021).
Developing SNPs and Strategies for Genomic Analysis in Alfalfa.
In: Yu LX., Kole C. (eds) The Alfalfa Genome. Compendium of Plant Genomes. Springer, Cham. https://doi.org/10.1007/978-3-030-74466-3_10

Ayala-Usma DA, Cárdenas M, Guyot R et al. (2021).
A whole genome duplication drives the genome evolution of Phytophthora betacei, a closely related species to Phytophthora infestans.
BMC Genomics 22: 795. https://doi.org/10.1186/s12864-021-08079-y

Kanapin A, Bankin M, Rozhmina T, Samsonova A, Samsonova M. (2021).
Genomic Regions Associated with Fusarium Wilt Resistance in Flax.
International Journal of Molecular Sciences 22(22):12383. https://doi.org/10.3390/ijms222212383

Bello JC, Hausbeck MK and Sakalidis ML. (2021).
Application of Target Enrichment Sequencing for Population Genetic Analyses of the Obligate Plant Pathogens Pseudoperonospora cubensis and P. humuli in Michigan.
Molecular Plant Microbe Interaction 34(10): 1103-1118. https://doi.org/10.1094/MPMI-11-20-0329-TA

Samsonova A, Kanapin A, Bankin M, et al. (2021).
A Genomic Blueprint of Flax Fungal Parasite Fusarium oxysporum f. sp. lini.
International Journal of Molecular Sciences 22(5):2665. https://doi.org/10.3390/ijms22052665

Diaz LM, Arredondo V, Ariza-Suarez D, et al. (2021).
Genetic Analyses and Genomic Predictions of Root Rot Resistance in Common Bean Across Trials and Populations.
Frontiers in Plant Science 12:629221. http://doi.org/10.3389/fpls.2021.629221

Islam M, Abdullah, Zubaida B, et al. (2021).
Agro-Morphological, Yield, and Genotyping-by-Sequencing Data of Selected Wheat (Triticum aestivum) Germplasm From Pakistan.
Frontiers in Genetics 12: 617772. https://doi.org/10.3389/fgene.2021.617772

García-Fernández C, Campa A, Garzón AS et al. (2021).
GWAS of pod morphological and color characters in common bean.
BMC Plant Biology 21: 184. https://doi.org/10.1186/s12870-021-02967-x

Negus KL, Chen L, Fresnedo-Ramírez J, Scott HA, Sacks GL, et al. (2021).
Identification of QTLs for berry acid and tannin in a Vitis aestivalis-derived 'Norton'-based population.
Fruit Research 1: 8. https://doi.org/10.48130/FruRes-2021-0008

Soler-Garzón A, McClean PE and Miklas PN. (2021).
Genome-Wide Association Mapping of bc-1 and bc-u Reveals Candidate Genes and New Adjustments to the Host-Pathogen Interaction for Resistance to Bean Common Mosaic Necrosis Virus in Common Bean.
Frontiers in Plant Science 12:699569. http://doi.org/10.3389/fpls.2021.699569

Akhatar J, Goyal A, Kaur N, et al. (2021).
Genome wide association analyses to understand genetic basis of flowering and plant height under three levels of nitrogen application in Brassica juncea (L.) Czern & Coss.
Scientific Reports 11: 4278. https://doi.org/10.1038/s41598-021-83689-w

Elias JCF, Gonçalves-Vidigal MC, Ariani A, et al. (2021).
Genome-Environment Association Analysis for Bio-Climatic Variables in Common Bean (Phaseolus vulgaris L.) from Brazil.
Plants. 2021; 10(8):1572. https://doi.org/10.3390/plants10081572

Grigoreva E, Barbitoff Y, Changalidi A, et al. (2021).
Development of SNP Set for the Marker-Assisted Selection of Guar (Cyamopsis tetragonoloba (L.) Taub.) Based on a Custom Reference Genome Assembly.
Plants 10(10):2063. https://doi.org/10.3390/plants10102063

Medina CA, Kaur H, Ray I and Yu LX. (2021).
Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.).
Cells 10(12): 3372. https://doi.org/10.3390/cells10123372

Lobaton J, Andrew R, Duitama J, et al. (2021).
Using RNA-seq to characterize pollen–stigma interactions for pollination studies.
Scientific Reports 11: 6635. https://doi.org/10.1038/s41598-021-85887-y

Nicolai T, Deparis Q, Foulquié-Moreno MR, et al. (2021).
In-situ muconic acid extraction reveals sugar consumption bottleneck in a xylose-utilizing Saccharomyces cerevisiae strain.
Microbial Cell Factories 20: 114. https://doi.org/10.1186/s12934-021-01594-3

Miedaner T, Vasquez A, Castiblanco V. et al. (2021).
Genome-wide association study for deoxynivalenol production and aggressiveness in wheat and rye head blight by resequencing 92 isolates of Fusarium culmorum.
BMC Genomics 22: 630. https://doi.org/10.1186/s12864-021-07931-5

Lin S, Medina CA, Norberg OS, et al. (2021)
Genome-Wide Association Studies Identifying Multiple Loci Associated With Alfalfa Forage Quality.
Frontiers in plant Science 12:648192. https://doi.org/10.3389/fpls.2021.648192

Deparis Q, Duitama J, Foulquié-Moreno MR, Thevelein JM. (2021).
Whole-Genome Transformation Promotes tRNA Anticodon Suppressor Mutations under Stress
mBio 12: 2. https://doi.org/10.1128/mBio.03649-20

Ocampo J, Ovalle T, Labarta R, et al. (2021).
DNA fingerprinting reveals varietal composition of Vietnamese cassava germplasm (Manihot esculenta Crantz) from farmers’ field and genebank collections.
Plant Molecular Biology in press. https://doi.org/10.1007/s11103-021-01124-0

Musker SD, Ellis AG, Schlebusch SA, Verboom GA. (2021).
Niche specificity influences gene flow across fine-scale habitat mosaics in Succulent Karoo plants
Molecular ecology 30(1): 175-192. https://doi.org/10.1111/mec.15721

Bassett A, Kamfwa K, Ambachew D and Cichy K. (2021).
Genetic variability and genome-wide association analysis of flavor and texture in cooked beans (Phaseolus vulgaris L.).
Theoretical and Applied Genetics 134: 959–978. https://doi.org/10.1007/s00122-020-03745-3

Gupta N, Gupta M, Akhatar J, et al. (2021).
Association genetics of the parameters related to nitrogen use efficiency in Brassica juncea L..
Plant Molecular Biology 105: 161–175. https://doi.org/10.1007/s11103-020-01076-x

Hill RJ, Baldassi C, Snelling JW, et al. (2021).
Fine mapping of the locus controlling self-incompatibility in European hazelnut.
Tree Genetics & Genomes 17: 6. http://doi.org/10.1007/s11295-020-01485-5

Bhattarai G, Fennel A, Londo JP, et al. (2021).
A Novel Grape Downy Mildew Resistance Locus from Vitis rupestris.
American Journal of Enology and Viticulture 72: 12-20. http://doi.org/10.5344/ajev.2020.20030

Cruz DF, De Meyer S, Ampe J, et al. (2020).
Using single‐plant‐omics in the field to link maize genes to functions and phenotypes.
Molecular systems biology 16: e9667. http://doi.org/10.15252/msb.20209667

Musker SD, Ellis AG, Schlebusch SA and Verboom GA. (2020).
Niche specificity influences gene flow across fine‐scale habitat mosaics in Succulent Karoo plants.
Molecular ecology 30(1) 175-192. http://doi.org/10.1111/mec.15721

Gil J, Herrera M, Duitama J, et al. (2020).
Genomic Variability of Phytophthora palmivora Isolates from Different Oil Palm Cultivation Regions in Colombia.
Phytopathology 110(9):1553-1564. http://doi.org/10.1094/PHYTO-06-19-0209-R

Valdisser PAMR, Muller BSF, de Almeida Filho JE, et al. (2020).
Genome-Wide Association Studies Detect Multiple QTLs for Productivity in Mesoamerican Diversity Panel of Common Bean Under Drought Stress.
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