Parts Group | Genetic screens of cellular traits

Parts Group | Genetic screens of cellular traits

Parts Group

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Our Research and Approach

We measure, model, and modulate cell state. We use genome engineering and synthetic biology to create cell lines that can be employed for CRISPR/Cas9-based genetic screening and high throughput cell biology assays. We develop probabilistic models as well as software tools to accurately analyse the readouts.

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People

Parts, Leopold
Dr Leopold Parts
Group Leader

Leo is a geneticist with broad interests in all areas of genomics. He focuses on figuring out most important features to measure in individual cells, creating accurate computational models for their readouts, and finding out how they are changed by genomes and environment. Trained in maths and computer science, he now spends his time pondering about statistics and high throughput cell biology.

Research Programmes

Partners and Funders

Internal Partners

Publications

  • Deep learning for computational biology.

    Angermueller C, Pärnamaa T, Parts L and Stegle O

    Molecular systems biology 2016;12;7;878

  • Quantitative CRISPR interference screens in yeast identify chemical-genetic interactions and new rules for guide RNA design.

    Smith JD, Suresh S, Schlecht U, Wu M, Wagih O et al.

    Genome biology 2016;17;45

  • Predicting quantitative traits from genome and phenome with near perfect accuracy.

    Märtens K, Hallin J, Warringer J, Liti G and Parts L

    Nature communications 2016;7;11512

  • Heritability and genetic basis of protein level variation in an outbred population.

    Parts L, Liu YC, Tekkedil MM, Steinmetz LM, Caudy AA et al.

    Genome research 2014;24;8;1363-70

  • Genome-wide mapping of cellular traits using yeast.

    Parts L

    Yeast (Chichester, England) 2014;31;6;197-205

  • gitter: a robust and accurate method for quantification of colony sizes from plate images.

    Wagih O and Parts L

    G3 (Bethesda, Md.) 2014;4;3;547-52

  • SGAtools: one-stop analysis and visualization of array-based genetic interaction screens.

    Wagih O, Usaj M, Baryshnikova A, VanderSluis B, Kuzmin E et al.

    Nucleic acids research 2013;41;Web Server issue;W591-6

  • Extent, causes, and consequences of small RNA expression variation in human adipose tissue.

    Parts L, Hedman ÅK, Keildson S, Knights AJ, Abreu-Goodger C et al.

    PLoS genetics 2012;8;5;e1002704

  • Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses.

    Stegle O, Parts L, Piipari M, Winn J and Durbin R

    Nature protocols 2012;7;3;500-7

  • Revealing the genetic structure of a trait by sequencing a population under selection.

    Parts L, Cubillos FA, Warringer J, Jain K, Salinas F et al.

    Genome research 2011;21;7;1131-8

  • Joint genetic analysis of gene expression data with inferred cellular phenotypes.

    Parts L, Stegle O, Winn J and Durbin R

    PLoS genetics 2011;7;1;e1001276

  • Functional Profiling of a Plasmodium Genome Reveals an Abundance of Essential Genes.

    Bushell E, Gomes AR, Sanderson T, Anar B, Girling G et al.

    Cell 2017;170;2;260-272.e8

  • Accurate Classification of Protein Subcellular Localization from High Throughput Microscopy Images Using Deep Learning.

    Pärnamaa T and Parts L

    G3 (Bethesda, Md.) 2017

  • Powerful decomposition of complex traits in a diploid model.

    Hallin J, Märtens K, Young AI, Zackrisson M, Salinas F et al.

    Nature communications 2016;7;13311

  • Deep learning for computational biology.

    Angermueller C, Pärnamaa T, Parts L and Stegle O

    Molecular systems biology 2016;12;7;878

  • Predicting quantitative traits from genome and phenome with near perfect accuracy.

    Märtens K, Hallin J, Warringer J, Liti G and Parts L

    Nature communications 2016;7;11512

  • Pathway-based factor analysis of gene expression data produces highly heritable phenotypes that associate with age.

    Anand Brown A, Ding Z, Viñuela A, Glass D, Parts L et al.

    G3 (Bethesda, Md.) 2015;5;5;839-47

  • Personalized medicine: from genotypes, molecular phenotypes and the quantified self, towards improved medicine.

    Dudley JT, Listgarten J, Stegle O, Brenner SE and Parts L

    Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 2015;342-6

  • Heritability and genetic basis of protein level variation in an outbred population.

    Parts L, Liu YC, Tekkedil MM, Steinmetz LM, Caudy AA et al.

    Genome research 2014;24;8;1363-70

  • Genome-wide mapping of cellular traits using yeast.

    Parts L

    Yeast (Chichester, England) 2014;31;6;197-205

  • A high-definition view of functional genetic variation from natural yeast genomes.

    Bergström A, Simpson JT, Salinas F, Barré B, Parts L et al.

    Molecular biology and evolution 2014;31;4;872-88

  • gitter: a robust and accurate method for quantification of colony sizes from plate images.

    Wagih O and Parts L

    G3 (Bethesda, Md.) 2014;4;3;547-52

  • High-resolution mapping of complex traits with a four-parent advanced intercross yeast population.

    Cubillos FA, Parts L, Salinas F, Bergström A, Scovacricchi E et al.

    Genetics 2013;195;3;1141-55

  • SGAtools: one-stop analysis and visualization of array-based genetic interaction screens.

    Wagih O, Usaj M, Baryshnikova A, VanderSluis B, Kuzmin E et al.

    Nucleic acids research 2013;41;Web Server issue;W591-6

  • Mapping cis- and trans-regulatory effects across multiple tissues in twins.

    Grundberg E, Small KS, Hedman ÅK, Nica AC, Buil A et al.

    Nature genetics 2012;44;10;1084-9

  • Quantifying selection acting on a complex trait using allele frequency time series data.

    Illingworth CJ, Parts L, Schiffels S, Liti G and Mustonen V

    Molecular biology and evolution 2012;29;4;1187-97

  • Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses.

    Stegle O, Parts L, Piipari M, Winn J and Durbin R

    Nature protocols 2012;7;3;500-7

  • Extent, causes, and consequences of small RNA expression variation in human adipose tissue.

    Parts L, Hedman ÅK, Keildson S, Knights AJ, Abreu-Goodger C et al.

    PLoS genetics 2012;8;5;e1002704

  • Revealing the genetic structure of a trait by sequencing a population under selection.

    Parts L, Cubillos FA, Warringer J, Jain K, Salinas F et al.

    Genome research 2011;21;7;1131-8

  • Assessing the complex architecture of polygenic traits in diverged yeast populations.

    Cubillos FA, Billi E, Zörgö E, Parts L, Fargier P et al.

    Molecular ecology 2011;20;7;1401-13

  • Joint genetic analysis of gene expression data with inferred cellular phenotypes.

    Parts L, Stegle O, Winn J and Durbin R

    PLoS genetics 2011;7;1;e1001276

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

    1000 Genomes Project Consortium, Abecasis GR, Altshuler D, Auton A, Brooks LD et al.

    Nature 2010;467;7319;1061-73

  • No correlation between childhood maltreatment and telomere length.

    Glass D, Parts L, Knowles D, Aviv A and Spector TD

    Biological psychiatry 2010;68;6;e21-2; author reply e23-4

  • A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies.

    Stegle O, Parts L, Durbin R and Winn J

    PLoS computational biology 2010;6;5;e1000770

  • Simultaneous assay of every Salmonella Typhi gene using one million transposon mutants.

    Langridge GC, Phan MD, Turner DJ, Perkins TT, Parts L et al.

    Genome research 2009;19;12;2308-16

  • Segregating YKU80 and TLC1 alleles underlying natural variation in telomere properties in wild yeast.

    Liti G, Haricharan S, Cubillos FA, Tierney AL, Sharp S et al.

    PLoS genetics 2009;5;9;e1000659

  • Population genomics of domestic and wild yeasts.

    Liti G, Carter DM, Moses AM, Warringer J, Parts L et al.

    Nature 2009;458;7236;337-41

  • Systematic discovery and characterization of fly microRNAs using 12 Drosophila genomes.

    Stark A, Kheradpour P, Parts L, Brennecke J, Hodges E et al.

    Genome research 2007;17;12;1865-79

  • Discovery of functional elements in 12 Drosophila genomes using evolutionary signatures.

    Stark A, Lin MF, Kheradpour P, Pedersen JS, Parts L et al.

    Nature 2007;450;7167;219-32

  • Evolution of genes and genomes on the Drosophila phylogeny.

    Drosophila 12 Genomes Consortium, Clark AG, Eisen MB, Smith DR, Bergman CM et al.

    Nature 2007;450;7167;203-18