Application of miRNA-seq in neuropsychiatry: A methodological perspective
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Hugo López-Fernández | Daniel Pérez-Rodríguez | Roberto C. Agís-Balboa | H. López-Fernández | R. Agís-Balboa | D. Pérez-Rodríguez
[1] A. Miyashita,et al. Serum microRNA miR-501-3p as a potential biomarker related to the progression of Alzheimer’s disease , 2017, Acta neuropathologica communications.
[2] Susumu Goto,et al. KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..
[3] Tyrone D. Cannon,et al. Insights into psychosis risk from leukocyte microRNA expression , 2016, Translational Psychiatry.
[4] Andrew E. Jaffe,et al. Bioinformatics Applications Note Gene Expression the Sva Package for Removing Batch Effects and Other Unwanted Variation in High-throughput Experiments , 2022 .
[5] Xiaowei Wang,et al. miRDB: an online database for prediction of functional microRNA targets , 2019, Nucleic Acids Res..
[6] J. Faraji,et al. Evidence for Ancestral Programming of Resilience in a Two-Hit Stress Model , 2017, Front. Behav. Neurosci..
[7] Boyang Li,et al. Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data , 2019, BMC Bioinformatics.
[8] T. Beach,et al. microRNA Profiles in Parkinson's Disease Prefrontal Cortex , 2016, Front. Aging Neurosci..
[9] Paul Theodor Pyl,et al. HTSeq—a Python framework to work with high-throughput sequencing data , 2014, bioRxiv.
[10] W. Huber,et al. which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets , 2011 .
[11] A. M. M. T. Reza,et al. microRNAs Mediated Regulation of the Ribosomal Proteins and its Consequences on the Global Translation of Proteins , 2021, Cells.
[12] Sharmila Banerjee-Basu,et al. AutDB: a gene reference resource for autism research , 2008, Nucleic Acids Res..
[13] Alexander R. Pico,et al. WikiPathways: connecting communities , 2020, Nucleic Acids Res..
[14] Matthew E. Ritchie,et al. limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.
[15] M. Robinson,et al. A scaling normalization method for differential expression analysis of RNA-seq data , 2010, Genome Biology.
[16] M. Ashburner,et al. Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.
[17] B. Strooper,et al. Non-coding RNAs with essential roles in neurodegenerative disorders , 2012, The Lancet Neurology.
[18] Sung-Chou Li,et al. Blood-Bourne MicroRNA Biomarker Evaluation in Attention-Deficit/Hyperactivity Disorder of Han Chinese Individuals: An Exploratory Study , 2018, Front. Psychiatry.
[19] D. Lancet,et al. GeneCards: integrating information about genes, proteins and diseases. , 1997, Trends in genetics : TIG.
[20] David J. Galas,et al. sRNAnalyzer—a flexible and customizable small RNA sequencing data analysis pipeline , 2017, Nucleic acids research.
[21] Sandrine Dudoit,et al. GC-Content Normalization for RNA-Seq Data , 2011, BMC Bioinformatics.
[22] S. Haggarty,et al. Diagnostic and therapeutic potential of microRNAs in neuropsychiatric disorders: Past, present, and future , 2017, Progress in Neuro-Psychopharmacology and Biological Psychiatry.
[23] Jin-hui Wang,et al. microRNA and mRNA profiles in nucleus accumbens underlying depression versus resilience in response to chronic stress , 2018, American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics.
[24] Zachary S. Lorsch,et al. Integrative Analysis of Sex-Specific microRNA Networks Following Stress in Mouse Nucleus Accumbens , 2016, Front. Mol. Neurosci..
[25] Yi Zhao,et al. Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts , 2013, Nucleic acids research.
[26] Terence P. Speed,et al. How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets , 2015, Nucleic acids research.
[27] Mark D. Robinson,et al. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..
[28] J. Quevedo,et al. MicroRNAs in Major Depressive Disorder. , 2019, Advances in experimental medicine and biology.
[29] H. Levin,et al. MicroRNA sequencing of rat hippocampus and human biofluids identifies acute, chronic, focal and diffuse traumatic brain injuries , 2020, Scientific Reports.
[30] Artemis G. Hatzigeorgiou,et al. DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA–gene interactions , 2017, Nucleic Acids Res..
[31] Ali Shojaie,et al. A comparative study of topology-based pathway enrichment analysis methods , 2019, BMC Bioinformatics.
[32] Xavier Estivill,et al. SeqBuster, a bioinformatic tool for the processing and analysis of small RNAs datasets, reveals ubiquitous miRNA modifications in human embryonic cells , 2009, Nucleic acids research.
[33] May D. Wang,et al. Effect of low-expression gene filtering on detection of differentially expressed genes in RNA-seq data , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[34] Haibo Liu,et al. Detecting Differentially Expressed Genes with RNA-seq Data Using Backward Selection to Account for the Effects of Relevant Covariates , 2015, Journal of Agricultural, Biological, and Environmental Statistics.
[35] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[36] Bin Xu,et al. MicroRNA dysregulation in neuropsychiatric disorders and cognitive dysfunction , 2012, Neurobiology of Disease.
[37] Patrick K. Kimes,et al. A practical guide to methods controlling false discoveries in computational biology , 2019, Genome Biology.
[38] Xia Li,et al. Expression alteration of microRNAs in Nucleus Accumbens is associated with chronic stress and antidepressant treatment in rats , 2019, BMC Medical Informatics Decis. Mak..
[39] Alicia Oshlack,et al. miRNA-Seq normalization comparisons need improvement. , 2013, RNA.
[40] Yogesh K. Dwivedi,et al. Exploiting Circulating MicroRNAs as Biomarkers in Psychiatric Disorders , 2020, Molecular Diagnosis & Therapy.
[41] J. Gill,et al. Circulating miRNA associated with posttraumatic stress disorder in a cohort of military combat veterans , 2017, Psychiatry Research.
[42] Jeffrey A. Thompson,et al. Common features of microRNA target prediction tools , 2014, Front. Genet..
[43] John D. McPherson,et al. Optimization of miRNA-seq data preprocessing , 2015, Briefings Bioinform..
[44] Bingqing Lin,et al. Stability of methods for differential expression analysis of RNA-seq data , 2019, BMC Genomics.
[45] Robert D. Finn,et al. Predicting active site residue annotations in the Pfam database , 2007, BMC Bioinformatics.
[46] David L. Wheeler,et al. GenBank , 2015, Nucleic Acids Res..
[47] P. Falkai,et al. microRNA‐34c is a novel target to treat dementias , 2011, The EMBO journal.
[48] Xiuqing Zhang,et al. Differential Expression of Plasma Exo-miRNA in Neurodegenerative Diseases by Next-Generation Sequencing , 2020, Frontiers in Neuroscience.
[49] Wei Zhang,et al. Identifying lncRNA–miRNA–mRNA networks to investigate Alzheimer’s disease pathogenesis and therapy strategy , 2020, Aging.
[50] Atul J. Butte,et al. Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges , 2012, PLoS Comput. Biol..
[51] Richard Durbin,et al. Fast and accurate long-read alignment with Burrows–Wheeler transform , 2010, Bioinform..
[52] W. Huber,et al. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.
[53] E. Hovig,et al. MirGeneDB 2.0: the metazoan microRNA complement , 2019, bioRxiv.
[54] J. Ruijter,et al. Small sample sizes in high-throughput miRNA screens: A common pitfall for the identification of miRNA biomarkers , 2017, Biomolecular detection and quantification.
[55] Adam J. Woods,et al. miRNA in Circulating Microvesicles as Biomarkers for Age-Related Cognitive Decline , 2017, Front. Aging Neurosci..
[56] Manolis Kellis,et al. PhyloCSF: a comparative genomics method to distinguish protein coding and non-coding regions , 2011, Bioinform..
[57] R. Giegerich,et al. Fast and effective prediction of microRNA/target duplexes. , 2004, RNA.
[58] R. Lu,et al. Serum miRNA as a possible biomarker in the diagnosis of bipolar II disorder , 2020, Scientific Reports.
[59] Differential blood miRNA expression in brain amyloid imaging-defined Alzheimer’s disease and controls , 2020, Alzheimer's Research & Therapy.
[60] M. Schatz,et al. Searching for SNPs with cloud computing , 2009, Genome Biology.
[61] Lior Pachter,et al. A direct comparison of genome alignment and transcriptome pseudoalignment , 2018, bioRxiv.
[62] Alex Bateman,et al. Non‐Coding RNA Analysis Using the Rfam Database , 2018, Current protocols in bioinformatics.
[63] Henning Hermjakob,et al. The Reactome pathway knowledgebase , 2013, Nucleic Acids Res..
[64] Xuegong Zhang,et al. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data , 2010, Bioinform..
[65] A. El-Osta,et al. Evaluation of microRNA alignment techniques , 2016, RNA.
[66] Ana Kozomara,et al. miRBase: from microRNA sequences to function , 2018, Nucleic Acids Res..
[67] Marcel Martin. Cutadapt removes adapter sequences from high-throughput sequencing reads , 2011 .
[68] Juliana Costa-Silva,et al. RNA-Seq differential expression analysis: An extended review and a software tool , 2017, PloS one.
[69] Eduardo Andrés-León,et al. miARma-Seq: a comprehensive tool for miRNA, mRNA and circRNA analysis , 2016, Scientific Reports.
[70] Pablo Tamayo,et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[71] R. Gentleman,et al. Independent filtering increases detection power for high-throughput experiments , 2010, Proceedings of the National Academy of Sciences.
[72] Vaibhav Shukla,et al. A compilation of Web-based research tools for miRNA analysis , 2017, Briefings in functional genomics.
[73] Wei Yang,et al. Investigating aberrantly expressed microRNAs in peripheral blood mononuclear cells from patients with treatment-resistant schizophrenia using miRNA sequencing and integrated bioinformatics , 2020, Molecular medicine reports.
[74] Sung-Chou Li,et al. miRSeq: A User-Friendly Standalone Toolkit for Sequencing Quality Evaluation and miRNA Profiling , 2014, BioMed research international.
[75] Cole Trapnell,et al. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome , 2009, Genome Biology.
[76] Seyed Hamid Aghaee-Bakhtiari,et al. miRandb: a resource of online services for miRNA research , 2017, Briefings Bioinform..
[77] Maciej Szymanski,et al. Noncoding RNAs database (ncRNAdb) , 2006, Nucleic Acids Res..
[78] Nicolas Servant,et al. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis , 2013, Briefings Bioinform..
[79] Brad T. Sherman,et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery , 2003, Genome Biology.
[80] Steven L Salzberg,et al. Fast gapped-read alignment with Bowtie 2 , 2012, Nature Methods.
[81] Thomas R. Gingeras,et al. STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..
[82] microRNA regulation of persistent stress-enhanced memory , 2018 .
[83] D. Bartel,et al. Predicting effective microRNA target sites in mammalian mRNAs , 2015, eLife.
[84] Jeffrey M. Perkel,et al. Workflow systems turn raw data into scientific knowledge , 2019, Nature.
[85] Ryan Gosselin,et al. Current RNA-seq methodology reporting limits reproducibility , 2019, Briefings Bioinform..
[86] B. Dean,et al. Changes in Non-Coding RNA in Depression and Bipolar Disorder: Can They Be Used as Diagnostic or Theranostic Biomarkers? , 2020, Non-coding RNA.
[87] Lior Pachter,et al. Sequence Analysis , 2020, Definitions.
[88] Yu Li,et al. MicroRNAs in Common Human Diseases , 2012, Genom. Proteom. Bioinform..
[89] Seyed Hamid Aghaee-Bakhtiari,et al. Web-based tools for miRNA studies analysis , 2020, Comput. Biol. Medicine.
[90] Yogesh Kumar Dwivedi,et al. Non-Coding RNAs in Psychiatric Disorders and Suicidal Behavior , 2020, Frontiers in Psychiatry.
[91] Liang Chen,et al. miRToolsGallery: a tag-based and rankable microRNA bioinformatics resources database portal , 2018, Database J. Biol. Databases Curation.
[92] Bradley C. Love,et al. Variability in the analysis of a single neuroimaging dataset by many teams , 2020, Nature.
[93] W. Filipowicz,et al. The widespread regulation of microRNA biogenesis, function and decay , 2010, Nature Reviews Genetics.
[94] Charlotte Soneson,et al. A comparison of methods for differential expression analysis of RNA-seq data , 2013, BMC Bioinformatics.
[95] Yang Yang,et al. Trends in the development of miRNA bioinformatics tools , 2019, Briefings Bioinform..
[96] A. Conesa,et al. Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package , 2015, Nucleic acids research.
[97] P. Shannon,et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.
[98] Sebastian D. Mackowiak,et al. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades , 2011, Nucleic acids research.
[99] Knut Reinert,et al. MicroRazerS: rapid alignment of small RNA reads , 2010, Bioinform..
[100] F. Borràs,et al. Exploratory study on microRNA profiles from plasma-derived extracellular vesicles in Alzheimer’s disease and dementia with Lewy bodies , 2019, Translational Neurodegeneration.
[101] Tim Beißbarth,et al. Comparative study on gene set and pathway topology-based enrichment methods , 2015, BMC Bioinformatics.
[102] Artemis G. Hatzigeorgiou,et al. DIANA-miRPath v3.0: deciphering microRNA function with experimental support , 2015, Nucleic Acids Res..
[103] Ming Wen,et al. miREvo: an integrative microRNA evolutionary analysis platform for next-generation sequencing experiments , 2012, BMC Bioinformatics.
[104] Johanna Hardin,et al. Selecting between‐sample RNA‐Seq normalization methods from the perspective of their assumptions , 2016, Briefings Bioinform..
[105] F. Middleton,et al. Salivary miRNA profiles identify children with autism spectrum disorder, correlate with adaptive behavior, and implicate ASD candidate genes involved in neurodevelopment , 2016, BMC Pediatrics.
[106] Piotr Zielenkiewicz,et al. Tools4miRs – one place to gather all the tools for miRNA analysis , 2016, Bioinform..
[107] M. Esteller. Non-coding RNAs in human disease , 2011, Nature Reviews Genetics.
[108] A. Oshlack,et al. Transcript length bias in RNA-seq data confounds systems biology , 2009, Biology Direct.
[109] David G Hendrickson,et al. Differential analysis of gene regulation at transcript resolution with RNA-seq , 2012, Nature Biotechnology.
[110] Lijuan Zhang,et al. Circulating Exosomal miRNA as Diagnostic Biomarkers of Neurodegenerative Diseases , 2020, Frontiers in Molecular Neuroscience.
[111] Jui-Hung Hung. Gene Set/Pathway enrichment analysis. , 2013, Methods in molecular biology.
[112] Ziad Obermeyer,et al. Regulation of predictive analytics in medicine , 2019, Science.
[113] F. Slack,et al. Sex-Dependent Changes in miRNA Expression in the Bed Nucleus of the Stria Terminalis Following Stress , 2019, Front. Mol. Neurosci..
[114] Anjali J. Koppal,et al. Supplementary data: Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites , 2010 .
[115] J. Kleinjans,et al. Circulating microRNAs as potential biomarkers for psychiatric and neurodegenerative disorders , 2019, Progress in Neurobiology.
[116] Norbert Gretz,et al. miRWalk - Database: Prediction of possible miRNA binding sites by "walking" the genes of three genomes , 2011, J. Biomed. Informatics.