Template switching mutations and RNA evolution
  • Mönttinen et al. 2023. Generation of de novo miRNAs from template switching during DNA replication. Proc Nat Acad Sciences USA 120:e2310752120 (link)
  • Löytynoja. 2022. Thousands of human mutation clusters are explained by short-range template switching. Genome Res 32: 1437-1447 (link)
  • Mönttinen and Löytynoja. 2022. Template switching in DNA replication can create and maintain RNA hairpins. Proc Nat Acad Sciences USA. 119:e2107005119 (link)
  • Löytynoja & Goldman. 2017. Short template switch events explain mutation clusters in the human genome. Genome Res 27, 1039-1049. (link)
Pinniped population genetics and speciation
  • Rosing-Asvid et al. 2023. An evolutionarily distinct ringed seal in the Ilulissat Icefjord. Mol Ecol 32:5932-5943 (link)
  • Olkkonen and Löytynoja. 2023. Analysis of population structure and genetic diversity in low-variance Saimaa ringed seals using low-coverage whole-genome sequence data. STAR protocols 4:102567 (link)
  • Löytynoja et al. 2023. Fragmented habitat compensates for the adverse effects of genetic bottleneck. Cur Biol 33:1009-1018 (link)
  • Savriama, Valtonen, Kammonen et al. 2018. Bracketing phenogenotypic limits of mammalian hybridization. R Soc Open Sci 5, 180903. (link)
Stickleback population genetics and evolutionary mechanisms
  • Kivikoski et al. 2023. Repeatability of crossover rate in wild sticklebacks. Biol Jour Lin Soc 140:74–84 (link)
  • Kivikoski et al. 2022. Predicting recombination frequency from map distance. Heredity 130:114-121 (link)
  • Feng, Merilä and Löytynoja. 2022. Complex population history affects admixture analyses in nine-spined sticklebacks. Mol Ecol 10.1111/mec.16651 (link)
  • Kivikoski et al. 2021. Automated improvement of stickleback reference genome assemblies with Lep‐Anchor software. Mol Ecol Res 21:2166-2176(link)
  • Kemppainen et al. 2021. Genetic population structure constrains local adaptation in sticklebacks. Mol Ecol 10.1111/mec.15808 (link)
  • Li et al. 2019. Effects of marker type and filtering criteria on QST-FST comparisons. R Soc Open Sci 6, 190666. (link)
  • Varadharajan et al. 2019. A High-Quality Assembly of the Nine-Spined Stickleback (Pungitius pungitius) Genome. Gen Biol Evol 11, 3291-3308. (link)
Software and analysis methods
  • Löytynoja. 2021. Phylogeny-Aware Alignment with PRANK and PAGAN. Methods Mol Biol (Clifton, N.J.). 2231, 17-37 (link)
  • Veidenberg and Löytynoja. 2021. Evolutionary Sequence Analysis and Visualization with Wasabi. Methods Mol Biol (Clifton, N.J.). 2231, 225-240 (link)
  • Veidenberg, Medlar, and Löytynoja. 2016. Wasabi: An Integrated Platform for Evolutionary Sequence Analysis and Data Visualization. Mol Biol Evol 33, 1126-1130. (link)
  • Tan, Gil, Löytynoja, Goldman, and Dessimoz. 2015. Simple chained guide trees give poorer multiple sequence alignments than inferred trees in simulation and phylogenetic benchmarks. Proc Nat Acad Sciences USA. 112, E99–100. (link)
  • Löytynoja. 2014. Phylogeny-aware alignment with PRANK. Methods Mol Biol 1079:155–170. (link)
  • Medlar, Aivelo, and Löytynoja. 2014. Séance: reference-based phylogenetic analysis for 18S rRNA studies. BMC Evol Biol 14, 235. (link)
  • Löytynoja, Vilella and Goldman. 2012. Accurate extension of multiple sequence alignments using a phylogeny-aware graph algorithm. Bioinformatics, 28:1684-1691.(link)
  • Löytynoja. 2012. Alignment methods: strategies, challenges, benchmarking, and comparative overview. Methods Mol Biol, 855:203-235.(link)
  • Löytynoja & Goldman. 2010. webPRANK: a phylogeny-aware multiple sequence aligner with interactive alignment browser. BMC Bioinformatics, 11:579.(link)
  • Löytynoja & Goldman. 2009. Uniting Alignments and Trees. Science, 324:1528–1529.(link)
  • Löytynoja & Goldman. 2008. Phylogeny-aware gap placement prevents errors in sequence alignment and evolutionary analysis. Science, 320:1632–1635.(link)
  • Löytynoja & Goldman. 2008. A model of evolution and structure for multiple sequence alignment. Phil Trans Royal Soc B, 363:3913–3919.(link)
  • Kankainen & Löytynoja. 2007. MATLIGN: a motif clustering, comparison and matching tool. BMC Bioinformatics, 8:189.(link)
  • Löytynoja & Goldman. 2005. An algorithm for progressive multiple alignment of sequences with insertions. Proc Nat Acad Sciences USA, 102:10557–10562.(link)
  • Löytynoja & Milinkovitch. 2003. A hidden Markov model for progressive multiple alignment. Bioinformatics, 19:1505–1513.(link)
Genomics and applied bioinformatic analyses
  • Wang et al. 2020. An inducible genome editing system for plants. Nature Plants 6, 766-772. (link)
  • Aivelo, Medlar, Löytynoja, Laakkonen, and Jernvall. 2015. Tracking year-to-year changes in intestinal nematode communities of rufous mouse lemurs (Microcebus rufus). Parasitology 142, 1095–1107. (link)
  • Buchmann, Löytynoja, Wicker and Schulman. 2014. Analysis of CACTA transposases reveals intron loss as major factor influencing their exon/intron structure in monocotyledonous and eudicotyledonous hosts. Mob. DNA 5, 24. (link)
  • Luo, Löytynoja & Moran. 2012. Genome content of uncultivated marine Roseobacters in the surface ocean. Envir Microbiol 14:41-51.(link)
  • ENCODE Project Consortium. 2007. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature, 447:799–816.(link)
  • Margulies et al. 2007. Analyses of deep mammalian sequence alignments and constraint predictions for 1% of the human genome. Genome Research, 17:760–774.(link)