Plant Genomics     


Plant Genomics - overview

Cacao Genome

The Cacao Genome collaboration has provided us with fascinating algorithmic challenges in plant genomics. The cacao genome and its application in mapping pod color was published in a Special Issue on Plant Genomics in Genome Biology [1]. 

The article has been accessed over 18,000 times (April 2019) and it has generated considarable online buzz ranking in top 5% of all research outputs scored by Altmetric.

An application of using ancestral recombination graphs for classification of cacao cultivars is presented in [3], and metholodogical advances are described below for haplotype inference [2] and assembly evaluation [4,5,6].

Haplotype Inference and Phenotype Association

We have designed iXora [2] (identifying crossovers and recombining alleles) for exact haplotype inference and trait association, see the iXora project page.

Haplotype Assembly of Polyploids

Haplotype assembly of polyploids is an open issue in plant genomics. Recent experimental studies have shown that available methods are not delivering satisfying results in practice. In one line of work we have developed a Gibbs sampling approach to the assembly problem [13]. In another line of work we have investigated models for haplotype assembly of tetraploid potato. This resulted in an Integer Linear Programming solution hosted on GitHub and research published in Bioinformatics [14].

Assembly Evaluation

Assembly of genomes now has become more of an art than science. As the collective community barrels through time assembling genomes at a rate more rapid than ever before, it becomes imperative to address the question: How good are the assemblies? We explore these questions in publications [4,5,6].

Genetic Trait Prediction

Whole genome prediction of complex phenotypic traits using high-density genotyping arrays is highly relevant to the fields of plant and animal breeding and genetic epidemiology. Given a set of plant, animal, or human biallelic molecular markers, such as SNPs, the goal is to predict the values of certain traits, usually highly polygenic and quantitative. Genomic selection involves simultaneously modeling all marker effects, in contrast to traditional GWAS. Our current work focuses on innovative methods to tackle this tough problem and address issues such as the high dimension of the data relative to the sample size, gene-by-environmental effects, epistatis, heritability, and the many computational challenges. Our current publications on this topic includes [7, 12].

Population Simulations

Constructing populations with pre-specified characteristics is a fundamental problem in population genetics and plant breeding, among others areas. One of the major challenges in handling realistic simulations for plant and animal breeding is the sheer number of markers. Due to advancing technologies, the requirement has quickly grown from hundreds of markers to millions. We present a scheme for representing and manipulating such realistic size genomes, without any loss of information [8].

SimBA* denotes algorithms for accurately simulating populations, with specific linkage and allele distributions for non-generative simulations, and recombination models for forward simulations [9]. See the SimBA project page for details.

Differential Gene Expression

RoDEO (Robust DE Operator) is our novel framework for detecting differentially expressed genes and stable genes between RNA-seq experiments [10], see the RoDEO project page.


Related Publications

  1. J. C. Motamayor*, K. Mockaitis*, J.Schmutz*, N. Haiminen*, D. Livingstone, O. Cornejo, S. D. Findley, P. Zheng, F. Utro, S. Royaert, C. Saski, J. Jenkins, R. Podicheti, M. Zhao, B. E. Scheffler, J. C Stack, F. A. Feltus, G. M. Mustiga, F. Amores, W. Phillips, J. Philippe Marelli, G. D. May, H. Shapiro, J. Ma, C. D Bustamante, R. J. Schnell, D. Main, D. Gilbert, L. Parida, D. N. Kuhn: The genome sequence of the most widely cultivated cacao type and its use to identify candidate genes regulating pod color. Genome Biology 14:6 R53, 2013. (* indicates joint first authors)
  2. F. Utro, N. Haiminen, D. Livingstone, O.E. Cornejo, S. Royaert, R. Schnell, J.C. Motamayor, D.N. Kuhn, L. Parida : iXora:Exact haplotype inferencing and trait association. BMC Genetics 14(1), 48, 2013.
  3. F. Utro, O.E. Cornejo, D. Livingstone, J.C. Motamayor, L. Parida: ARG-based genome-wide analysis of cacao cultivars. BMC Bioinformatics 13(Suppl 19), S17, 2012.
  4. F.A. Feltus, C.A. Saski, K. Mockaitis, N. Haiminen, L. Parida, Z.M. Smith, J.B. Ford, M.E. Staton, S.P. Ficklin, B.P. Blackmon, R.J. Schnell, D.N. Kuhn , J.-C. Motamayor: Sequencing of a QTL-rich Region of the Theobroma cacao Genome using Pooled BACs, BMC Genomics, 2011.
  5. N. Haiminen, D. Kuhn, L. Parida, I. Rigoutsos: Evaluation of Methods for De Novo Genome Assembly from High-Throughput Sequencing Reads Reveals Dependencies That Affect the Quality of the Results, PLoS ONE, 2011.
  6. N. Haiminen, F.A. Feltus, L. Parida: Assessing Pooled BAC and Whole Genome Shotgun Strategies for Complex Genome Assembly, BMC Genomics, 2011.
  7. D. He, I. Rish, D. Haws, S. Teyssedre, Z. Karaman, L. Parida: MINT: Mutual Information based Transductive Feature Selection for Genetic Trait Prediction, MLSB workshop 2013. pdf here
  8. N. Haiminen, F. Utro, C. Lebreton, P. Flament, Z. Karaman, L. Parida: Efficient in silico Chromosomal Representation of Populations via Indexing, Algorithms 6(3), pp. 430-441, 2013.
  9. N. Haiminen, C. Lebreton, L. Parida: Best-Fit in Linear Time for Non-generative Population Simulation. Algorithms in Bioinformatics, in Lecture Notes in Computer Science 8701, 247-262, Springer, 2014.
  10. Niina Haiminen, Manfred Klaas, Zeyu Zhou, Filippo Utro, Paul Cormican, Thomas Didion, Christian Sig Jensen, Chris Mason, Susanne Barth, Laxmi ParidaComparative Exomics of Phalaris cultivars under salt stress. BMC Genomics 15(Suppl 6):S18, 2014. 
  11. J. Alberto Romero Navarro, Wilbert Wilbert Phillips-Mora, Adriana Arciniegas-Leal, Allan Mata-Quiros, Niina Haiminen, Guiliana Mustiga, Donald Livingstone III, Harm Van bakel, David Kuhn, Laxmi Parida, Andrew Kasarskis and Juan Carlos Motamayor: Application of genome wide association and genomic prediction for improvement of cacao productivity and resistance to black and frosty pod diseasesFrontiers in Plant Science, 2017.
  12. D. He, D. Kuhn, L. Parida: Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction. Bioinformatics, 32(12), 2016.
  13. D. He, S. Saha, R. Finkers, L. Parida: Efficient algorithms for polyploid haplotype phasing. BMC Genetics, BioMed Central Ltd, 2018.
  14. Enrico Siragusa, Niina Haiminen, Richard Finkers, Richard Visser, Laxmi Parida: Haplotype assembly of autotetraploid potato using integer linear programming. Bioinformatics, 2019.