Bioinformatics Resources






Papers on Sequencing & Alignment

  • Thomas P. Niedringhaus, et al., Landscape of Next-Generation Sequencing Technologies, Analytical Chemistry 83, 2011
  • Erwin L. van Dijk, et al., Ten years of next-generation sequencing technology, Trends in Genetics 30(9), 2014
  • Heng Li and Nils Homer, A survey of sequence alignment algorithms for next-generation sequencing, Briefings in Bioinformatics 2(5), 2010
  • Matei Zaharia, et al., Faster and More Accurate Sequence Alignment with SNAP, arXiv:1111.5572v1, 2011
  • Michael R. Wilson, et al., Actionable Diagnosis of Neuroleptospirosis by Next-Generation Sequencing, The New England Journal of Medicine, 2014, DOI:10.1056/NEJMoa1401268
  • Samia N. Naccache, et al., A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples, Genome Research, 2014, doi:10.1101/gr.171934.113

Papers on Autism Spectrum Disorder

  • Meng-Chuan Lai,, Autism, The Lancet 2014, volume 383, issue 9920, doi:10.1016/S0140-6736(13)61539-1
  • Bernie Devline, Stephen W Scherer, Genetic architecture in autism spectrum disorder, Curr Opin Genet Dev (2012) 22:1-9
  • Catalina Betancur, Etiological heterogeneity in autism spectrum disorders: More than 100 genetic and genomic disorders and still counting, Brain Research 1380 (2011) 42-77
  • Danial H. Ebert & Michael E. Greenberg, Activity-dependent neuronal signalling and autism spectrum disorder, Nature 493 (2013)
  • Ivan Iossifov, et al., De Novo Gene Disruptions in Children on the Autistic Spectrum, Neuron 74 (2012)
  • Benjamin M. Neale, et al., Patterns and rates of exotic de novo mutations in autism spectrum disorders, doi:10.1038/nature11011
  • Brain J. O’Roak, et al., Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations, doi:10.1038/nature10989
  • Yong-hui Jiang, et al., Detection of Clinically Relevant Genetic Variants in Autism Spectrum Disorder by Whole-Genome Sequencing, The American Journal of Human Genetics 93 (2013)
  • Stephan J. Sanders, et al., De novo mutations revealed by whole-exome sequencing are strongly associated with autism, doi:10.1038/nature10945
  • E Ben-David and S Shifman, Combined analysis of exome sequencing points toward a major role for transcription regulation during brain development in autism, doi:10.1038/mp.2012.148
  • Dalila Pinto, et al., Functional impact of global rare copy number variation in autism spectrum disorders, doi:10.1038/nature09146
  • Sebastien Jacquemont et al., A Higher Mutational Burden in Females Supports a “Female Protective Model” in Neurodevelopmental Disorders, The American Journal of Human Genetics 94 (2014)
  • Benjamin Georgi et al., From Mouse to Human: Evolutionary Genomics Analysis of Human Orthologs of Essentials Genes, PLoS Genet 9(5), 2013
  • Kou Y, Betancur, et al., Network- and Attribute-Based Classifiers can Prioritize Genes and Pathways for Autism Spectrum Disorders and Intellectual Disability, American Journal of Medical Genetics Part C 160C, 2012
  • Sarah R. Gilman, et al., Rare De Novo Variants Associated with Autism Implicate a Large Functional Network of Genes Involved in Formation and Function of Synapses, Neuron 70, 2011
  • Sarah R Gilman, et al., Diverse types of genetic variation converge on functional gene networks involved in schizophrenia, Nature Neuroscience 2012
  • Susan Walker and Stephen W Scherer, Identification of candidate intergenic risk loci in autism spectrum disorder, BMC Genomics 2013

Papers on Genomic Structural Variation

  • Tam P. Sneddon and Deanna M. Church, Online Resources for Genomic Structural Variation, Methods Mol Biol. 2012 ; 838: 273–289. doi:10.1007/978-1-61779-507-7_13
  • Alexej Abyzov et al., CNVnator: An approach to discover, genotype and characterize typical and atypical CNVs from family and population genome sequencing, Genome Res. 21 (2011), doi:10.1101/gr.114876.110
  • Jacob J Michaelson & Jonathan Sebat, forestSV: structural variant discovery through statistical learning, Nature Methods 9 (2012), doi:10.1038/NMETH.2085
  • Seungtai Yoon et al., Sensitive and accurate detection of copy number variants using read depth of coverage, Genome Research 19 (2009)
  • Min Zhao et al., Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives, BMC Bioinformatics 14 (2013)
  • Ryan E. Mills et al., Mapping copy number variation by population-scale genome sequencing, Nature 470 (2011), doi:10.1038/nature09708
  • D’Antonio M, Ciccarelli FD, Modification of Gene Duplicability during the Evolution of Protein Interaction Network, PLoS Comput Biol 7(4), 2011, doi:10.1371/journal.pcbi.1002029
  • Takashi Makino et al., Genome-wide deserts for copy number variation in vertebrates, Nature Communications 2013, doi:10.1038/ncomms3283
  • Donald F. Conrad, et al., Origins and functional impact of copy number variation in the human genome, Nature vol. 464, 2010
  • Benjamin Schuster-Bockler, et al., Dosage Sensitive Shapes the Evolution of Copy-Number Varied Regions, PLoS ONE 5(3): e9474. doi:10.1371/journal.pone.0009474
  • Soumya Raychaudhuri, et al., Identifying Relationships among Genomic Disease Regions: Predicting Genes at Pathogenic SNP Associations and Rare Deletions, PLoS Genetics 5(6), 2009
  • G. Kirov, et al., A genome-wide association study in 574 schizophrenia trios using DNA pooling, Molecular Psychiatry 14, 2009
  • Gregory Costain, et al., Pathogenic rare copy number variants in community-based schizophrenia suggest a potential role for clinical microarrays, Human Molecular Genetics, 2013
  • Candice K. Silversides, et al., Rare Copy Number Variations in Adults with Tetralogy of Fallot Implicate Novel Risk Gene Pathways, PLoS Genetics 8(8) 2012
  • Ekta Khurana, et al., Integrative Annotation of Variants from 1092 Humans: Application to Cancer Genomics, Science 342, 2013

Papers on Interactome Analysis

  • Uri Alon, Network motifs: theory and experimental approaches, Nature Reviews Genetics 8, 2007
  • Sebastian Kohler, et al., Walking the Interactome for Prioritization of Candidate Disease Genes, The American Journal of Human Genetics 82, 2008
  • Sinan Erten, et al., DADA: Degree-Aware Algorithms for Network-Based Disease Gene Prioritization, BioData Mining 4:19, 2011
  • Saket Navlakha and Carl Kingsford, The power of protein interaction network for associating genes with diseases, Bioinformatics 26(8), 2010

Papers on Genome Annotation

  • Mark B. Gerstein, et al., Architecture of the human regulatory network derived from ENCODE data, Nature 489, 2012
  • The Encode Project Consortium, An integrated encyclopedia of DNA elements in the human genome, Nature 489, 2012
  • Th FANTOM Consortium, A Promoter-level mammalian expression atlas, Nature 507, 2014
  • Robin Andersson, et al., An atlas of active enhancers across human cell types and tissues, Nature 507, 2014
  • G. Natoli and J.C. Andrau, Noncoding Transcription at Enhancers: General Principles and Functional Models, Annual Review of Genetics, 2012

Papers on Synthetic Genome

  • Monya Baker, The next step for the synthetic genome, Nature 473 (2011)

Papers on Forensics

  • Jacqueline Weber-Lehmann, et al., Finding the needle in the haystack: Differentiating “identical” twins in paternity testing and forensics by ultra-deep next generation sequencing, FSI Genetics, 2013


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