2013
Adjusting for Background Mutation Frequency Biases Improves the Identification of Cancer Driver Genes
Evans P, Avey S, Kong Y, Krauthammer M. Adjusting for Background Mutation Frequency Biases Improves the Identification of Cancer Driver Genes. IEEE Transactions On NanoBioscience 2013, 12: 150-157. PMID: 23694700, PMCID: PMC3989533, DOI: 10.1109/tnb.2013.2263391.Peer-Reviewed Original ResearchConceptsMore non-synonymous mutationsMutation frequencyTumor sequencing projectsGene-specific mannerCancer driver genesNon-synonymous mutationsSynonymous mutation ratioMutation biasSequencing projectsBackground mutation frequencyGene expressionDriver genesGenesTumor developmentMutation burdenMutation ratioHigher non-synonymous mutation burdenMutationsMutation countsExpressionBackground frequencyFrequency biasesIdentification
2011
AlleleSeq: analysis of allele‐specific expression and binding in a network framework
Rozowsky J, Abyzov A, Wang J, Alves P, Raha D, Harmanci A, Leng J, Bjornson R, Kong Y, Kitabayashi N, Bhardwaj N, Rubin M, Snyder M, Gerstein M. AlleleSeq: analysis of allele‐specific expression and binding in a network framework. Molecular Systems Biology 2011, 7: msb201154. PMID: 21811232, PMCID: PMC3208341, DOI: 10.1038/msb.2011.54.Peer-Reviewed Original ResearchMeSH KeywordsAllelesCell LineChromosome MappingChromosomes, Human, XChromosomes, Human, YDatabases, GeneticDNA-Binding ProteinsGene Expression RegulationGene Regulatory NetworksGenome, HumanHumansMolecular Sequence AnnotationOligonucleotide Array Sequence AnalysisPolymorphism, Single NucleotideSequence Analysis, RNATranscription FactorsConceptsAllele-specific expressionGenome sequenceFunctional genomics data setsAllele-specific behaviorAllele-specific eventsDiploid genome sequenceChIP-seq data setsGenomic data setsGenomic sequence variantsPersonal genome sequencesAlignment of readsRNA-seqGenome ProjectPaternal alleleComputational pipelineReads mappingSequence variantsNetwork motifsVariation dataReference alleleAllelesReadsSequenceExpressionMaternally
2005
MicroRNA: Biological and Computational Perspective
Kong Y, Han J. MicroRNA: Biological and Computational Perspective. Genomics Proteomics & Bioinformatics 2005, 3: 62-72. PMID: 16393143, PMCID: PMC5172550, DOI: 10.1016/s1672-0229(05)03011-1.Peer-Reviewed Original ResearchConceptsAbundant gene familyNon-coding RNAsDiscovery of miRNAMulticellular speciesGene familyMiRNA researchGene expressionBiological processesRegulatory functionsMiRNAComputational approachMicroarray applicationsComputational methodsMiRNAsDiscoveryMicroRNAsRNANucleotidesSpeciesPlantsIndispensable toolExpressionFamily